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Hi there. I'm trying to make SAOM work with an innovation network based on cooperation cuz it owns advantages on dealing with longitudinal networks. And here's something I'm a bit confused.
1. How could I tell if my data is suitable for this model? Are there any specific metrics?
2. I was planning to use SAOM to explore the effect of multidimensional proximity on the evolution of innovation cooperation networks over 4 time observations:
array(c(N1, N2, N3, N4), dim = c(59, 59, 4),
which means it is an undirected, longitudinal type. But it required 3 (waves)input observation network data as a dyadic covariate(?). So is it proper way if I add the change of collaboration times(weights of the edges) between two actors for each period as: edge→varDyadCovar→array (c(edge1-2, edge2-3, edge3-4), dim= c(59 ,59 ,3)?
3. If I want to test the effect of the proximity indicator on the network evolution, is it necessary to add him to the model at the beginning:
mydata <- sienaDataCreate(cooperation,
GDP, urban,
edge,
geo)
or use it in a subsequent commanding:
myeff <- includeEffects(myeff,interaction1="GDP")
with command like this?
4. In the official RSiena manaul, it is mentioned that 'A default model choice could consist of the outdegree(density) and reciprocity effect', but my output only contains the rate of network evolution and outdegree calculation results. How could I view my input variables influences in the Siena-table?
Looking forward to your thoughts and thanks in advance!
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Look the link, maybe useful.
Regards,
Shafagat
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Hello everyone.
I have questions regarding comparing frequencies between groups. I will be happy if someone can help.
So I will describe first briefly my research design:
- I am analysing online shaming that has per se 10 types (10 types of shaming).
- I am analyzing 6 cases (multiple case studies) of shaming events.
- I am using thematic analysis.
- In the data (comments from social media) I am analyzing how many types of shaming occur in each data.
- In each case, I have obtained by thematic analyses how many times each type of shaming occurs (per se type 1 occurs 50x times in case 1, type 2 occurs 124 times in case 1 - type 1 occurs 12 times in case 2, type 2 occurs 32 times in case 2 etc).
- The 6 cases will be grouped into three groups by the theory, so I will have three groups (in one group there will be 2 cases, in the second group there will be another 2 cases and in the third group there will also be the other 2 cases).
- I want to compare the frequencies of types of shaming between these three groups.
So how do we compare frequencies/proportions between groups?
I must notion that the number of all codings was different in individual cases. For example in example 1 the number of encodings was - say 1,200, in example 2 the number of encodings was - say 800. A number of codings = number of all codings related to the types of shaming. So I can't just count these frequencies, but I have to weigh them. Does anyone have an idea how to compare the frequencies between different cases where the numerus are different?
Thank you so much for your help.
SHORT QUESTION: How to compare frequencies between groups where in each group there are different cases and each case has a different number of total codings (thematic analysis)?
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I don't know this method and I cannot tell you if this would be legit in your case. This seems a topic in linguistic research (I am not a linguist). It seems the method of choice when your frequencies are from different "corpora" (whatever this means) and when you want to campare these corpora (not directly the word frequency distributions). But as I said: this is beyond my expertise.
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What are the basic methods for collection of data in social network.
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Dear Mr. Srivastav!
My logic is that by providing the following case studies you can access the research methodology section within these publications and look at the details you consider appropriate:
1) Casañ, R.R., García-Vidal, E., Grimaldi, D. et al. Online polarization and cross-fertilization in multi-cleavage societies: the case of Spain. Soc. Netw. Anal. Min. 12, 79 (2022). https://doi.org/10.1007/s13278-022-00909-5 Open access:
2) Fionda, V., Madi, S.A. & Pirrò, G. Community deception: from undirected to directed networks. Soc. Netw. Anal. Min. 12, 74 (2022). https://doi.org/10.1007/s13278-022-00896-7, Open access:
3) Belcastro, L., Branda, F., Cantini, R. et al. Analyzing voter behavior on social media during the 2020 US presidential election campaign. Soc. Netw. Anal. Min. 12, 83 (2022). https://doi.org/10.1007/s13278-022-00913-9, Open access: '
4) Pallotti Francesca, Sharon Marie Weldon, Alessandro Lomi,
Lost in translation: Collecting and coding data on social relations from audio-visual recordings, Social Networks, Volume 69, 2022, Open access:
5) Alina Lungeanu, Mark McKnight, Rennie Negron, Wolfgang Munar, Nicholas A. Christakis, Noshir S. Contractor, Reprint of: Using Trellis software to enhance high-quality large-scale network data collection in the field, Social Networks, Volume 69, 2022, Open access:
6) Patrycja Stys, Samuel Muhindo, Sandrine N’simire, Ishara Tchumisi, Papy Muzuri, Bauma Balume, Johan Koskinen, Reprint of: Trust, quality, and the network collection experience: A tale of two studies on the Democratic Republic of the Congo, Social Networks, Volume 69, 2022, Open access:
Yours sincerely, Bulcsu Szekely
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Dear Colleagues,
I have a very generic question, more prone to be a discussion. As you know, the number of papers devoted to modular networks (networks with communities or partitioned networks) is increasing. The majority of research is about detection of communities. My curiosity is the following: There are papers dealing with the problem of ranking nodes across communities. For instance, a generic ranking can be obtained as an aggregate form of rankings locally obtained within each community (using some centrality measures). When you aggregate rankings obtained within different communities, you can use for instance "primus inter pares" style. For instance, you can compare nodes in different communities only if they have earned the same rank in their different communities. This is a simple idea and, for sure, not the unique idea. I have been searching for papers dealing with those types of conceptualisation or applications on modular networks but I haven't found very much on this issue. For instance, Something similar is done when they compare teams in different European football leagues (communities) since they have to form "qualification groups" for the final part of the Champions league (across communities). But they use scores obtained within the specific football league. Thanks very much for your attention.
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Have you looked into "social network analysis"? Lot of basics contained there that you would need to know regarding your questions. BTW, most human based networks are not very modular (i.e. disjoint) they are overlapping/intersecting(common membership)... so a whole new level of complexity is introduced which requires fuzzy set theory.
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I'm looking for datasets containing coherent sets of tweets related to Covid-19 (for example, collected within a certain time period according to certain keywords or hashtags), containing labels according to the fact they contain fake/real news, or according to they fact they contain pro-vax / anti-vax information. Possibly, the dataset I'm looking for would also contain a column showing the textual content of each tweet, a row showing the date, and columns showing 1)The username /id of the autohor; 2)The username/id of the people who retweeted the tweet.
Do you know any dataset with these features?
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@Luigi Arminio,
This attached screenshot maybe useful. Thanks~PB
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Exploring the similarities and differences between these three powerful machine learning tools (PCA, NMF, and Autoencoder) has always been a mental challenge for me. Anyone with knowledge in this field is welcome to share it with me.
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In machine learning projects we often run into curse of dimensionality problem where the number of records of data are not a substantial factor of the number of features. This often leads to a problems since it means training a lot of parameters using a scarce data set, which can easily lead to overfitting and poor generalization. High dimensionality also means very large training times. So, dimensionality reduction techniques are commonly used to address these issues. It is often true that despite residing in high dimensional space, feature space has a low dimensional structure.
Regards,
Shafagat
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I'm looking forward to studying the hypersexualization on TikTok using a descriptive approach and case study method, however I have a problem in choosing my pursposive sample:
- I have a list of challenges (the so-called sexy challenges) that represent the main features and commonalities but from 2019 to 2021.
- I have a list of the most followed Tiktokers with a hypersexualized content most of the time.
In your opinion which one should I use?
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In order to help you, we need to know your research question, your research problem. Decisions on methods and research techniques come out from the research problem. In your case, it is not easy to clarify hypersexualization: is it related to public space? Social networks? Media? Gender? Discourse? Relations?Representations? There is a need to explain the subject and thereafter justify the Tik Tok choice: is it basically diverse from the gift-giving / gift-receiving socio-symbolic structure of all social networks? Nonetheless, your general topic does not seem like a "case study," which is a strategy really for cases with a "casual" scale: Tik Tok does not seem like a singular case...
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Social Network Analysis
digital content analysis
Audience Study
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For Social Network Analysis, its better you arrange the sample data from top influncers and then compare with yours, you can use locobuzz & talkwalker
For Digital Content, Semrush & Ahrefs is best
For Audience, Google Analytics is always preferred until you have any MMP integrated
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I try to help a doctoral student (my daughter, actually) to do a meta-analysis on a medical topic. I would like to apply social network analysis to a bibliographic survey (authors, key-words, abstract and citations). I plan to use the CiteSpace application which looked to me a good option at first glance. Unfortunately for me, being an independent researcher, I have no access to institutional databases and must use open source such as Google Scholar for my bibliographic research.
But the software is mainly dedicated at analysing Web of Science type of data, even if the manual mentions the option of using bibliographic records from other licensed sites like PubMed (with the same issue of requiring licensed access).
Do you know ways of formatting Google Search results according to Web of Science format, either by hand (csv file) or using dedicated applications (e.g., R scripts)?
Thanks in advance, Hubert
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PubMed
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I'm searching about autoencoders and their application in machine learning issues. But I have a fundamental question.
As we all know, there are various types of autoencoders, such as ​Stack Autoencoder, Sparse Autoencoder, Denoising Autoencoder, Adversarial Autoencoder, Convolutional Autoencoder, Semi- Autoencoder, Dual Autoencoder, Contractive Autoencoder, and others that are better versions of what we had before. Autoencoder is also known to be used in Graph Networks (GN), Recommender Systems(RS), Natural Language Processing (NLP), and Machine Vision (CV). This is my main concern:
Because the input and structure of each of these machine learning problems are different, which version of Autoencoder is appropriate for which machine learning problem.
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Look the link, maybe useful.
Regards,
Shafagat
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Software Experts: Ever wanted to write a book? Here's an opportunity close to it that you may not want to miss. Please see
for more details.
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Thank you.
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I have a task on my social networks analysis course and I need to find a full dataset with the Medici family and its attributes. I'd be glad for any help.
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If you are conducting a social network analysis (SNA) to identify a knowledge broker within a project, is it okay to describe the project in broad terms within your paper? Or, does it always have to be anonymous? If you know any examples where an SNA paper describes the project, can you link it for me? Thanks!
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Hi,
Students’ Intention to Use Social Media for Academic Purposes: A Study of South Eastern University of Sri Lanka
Social media adoption: small and medium-sized enterprises’ perspective in Sri Lanka
Refer these articles from my Research Gate profile
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For my doctoral research, I have a dataset of 8 teams, with 2 teams each from 4 organizations, and I am checking peer centrality in team advice networks using centrality. These are directed networks. I have created adjacency matrices and each matrix has 12 to 30 nodes.
Please advice:
  1. Should I test each team network individually or club them to get organizational correlations? Should be there any other partition applied?
  2. What should be my main considerations when working with visualization of small networks?
3.I used the Yi-Fan Hu layout (output) for betweenness centrality related to general workplace advice when I ran the first trials. What should I be using for best rendering?
4. What tests should I run and should I report it in writing?
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hi.,
Kindly Check this link:
Best wishes..
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How do we calculate data processing time, response time and cost in cloud analyst simulator?
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Hello all!
Please send me some article for "processing time on cloud computing: define, calculation, silulation,... Thank you very much!
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I have started writing an article related to "Narcissism" and its effect on "Social Media". Can anyone suggest a good quality journal where I can submit it. Basically, it will be a systematic literature review paper.
I do not finish my writing yet, so I am not able to share my title or abstract yet.
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Any journal indexed in scopus
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Hello everybody
I am solving a Social Network Analysis problem. I have 9 centrality measures in my problem and I am trying to combine them for creating a new centrality measure.
I have chosen TOPSIS as a combining method. Now I am looking for an easy method to assign appropriate weights to my criteria.
If you think you can help me and even introduce me to a better solution than TOPSIS, I will be glad if you share it with me.
Best Regards
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I suggest using entropy derived weights that are objective
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What are the main feature you would extract from a social netowrk to model wellbeing and mental health.
Also what the common formulas for the following features: Engagment, popularity, participation, ego.
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Eiman Kanjo Here is a paper which created Mental Well-Being Index https://precog.iiitd.edu.in/pubs/mental-wellbeing-2017.pdf and analyzed college campuses in the US using Reddit.... Hope this is helpful... Feel free to reach out if you need any further information..
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Social Network Analysis Datasets Needed?
Dear All,
I need the following datasets and/or any datasets that has one or more of the following features.
The datasets should allow interaction among Online social users, recommender systems and Online social network server or in a decentralized systems.
Other static profiles (e.g. interests, locations) that could be preserved by privacy schemes.
Thank you in advance.
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Look the link, maybe useful.
Regards,
Shafagat
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So, I am wanting to take network data from various time periods of the same network and map it in a way that will allow for clear representation of each time period. For example, taking network data for a 2013-2015 time period, a 2015-217 time period and then a 2020-2021 time period. Importantly, the amount of data will be different and the time periods won't strictly be even (i.e. there might be a 1 year gap between time period a and b and then 5 years between b and c). Also, I think it is important to highlight that these would be 'time-slices' of the same overall network, rather than distinct, unrelated networks.
I am hoping to present these different time-slices in one or two ways. First, I am wanting to be able to place a series of network maps next to each other based on temporal data to show how the network is changing over time. Second, if possible, I would like to produce a video/animation that shows the network changing over time.
I have been doing lots of reading on possible ways to achieve this type of analysis. I have been looking into using stochastics, specifically Markov models or Stochastic Actor-Orientated Modelling (SAOM), both of which I have seen used for similar projects. Only problem is my maths is good, but needs some work before I could comfortably use these approaches, so if stochastics is the way forward, any suggestions for good tutorials?
I have also been looking into Social Sequence Analysis, specifically the use of Network Methods, as outlined by Cornwell (https://www.cambridge.org/core/books/social-sequence-analysis/network-methods-for-sequence-analysis/FFF842AF37364167E23AD03E50650336) which seems promising.
I feel as though I am reading a lot and getting a bit lost in all of the literature. Any advice would be greatly appreciated!
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I thought it would be worth commenting on this in case anyone comes across this question and wants an answer.
After more extended reading I came across temporal network theory and it has solved all of my problems. If you are wanting to do something similar to what I described in my original question then I strongly recommend reading Holme and Saramäki's book Temporal Network Theory (https://www.springer.com/gp/book/9783030234942) and then look at this excellent tutorial about how to create temporal networks in R: https://programminghistorian.org/en/lessons/temporal-network-analysis-with-r or this guide to Teneto if you're a Python person: https://teneto.readthedocs.io/en/latest/what_is_tnt.html
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Does data from contact tracing help in establishing patterns of behavior and social interactions that lead to infections? There are cases here in the Philippines where patients have no travel history but still get the virus. It is probable that patterns of behavior of other members of the household (for example, working in enclosed and densely populated workspaces) might cause the infection. Just a thought.
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You might find of interest this white paper of how airborne diseases spread and can be tracked by contact tracing and visualized by network analysis
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Dear all,
I would like to know if it is possible to use SAOMs (Stochastic actor oriented models) to analyse weighted networks?
Thank you in advance,
Léa DAUPAGNE
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There seems to be a less ised feature to handle ordinal values. It is discussed here: http://lists.r-forge.r-project.org/pipermail/rsiena-help/2013-July/000272.html
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How can we consider linguistic differences in the analysis of Twitter data?
I'm trying to use tweet data as a proxy for mental health status worldwide. How can I minimize the bias due to linguistic differences as I may miss some data in languages other than English?
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I agree with Mohamed Elhadad . First level of differences is the difference in language between or witin regions of the world. Even if you use countries as unit of observation, you will encounter different langauges for some countries. You could try to just filter english tweets from the regions, probably reflecting in a biased sample. Second level are the regional differences you particularly want to account for. Last but not least it is important to note that Twitter "slang" can deviate from regular communication behavior and thus it would be wise to link "real-life" evidence of mental health issues (or indicators) to online behavior. I think this is a really hard job when considering level 1 and 2 and doing it for "all the world". Nevertheless, this paper might answer a part of your question or:
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The field of social network analysis and all its quantitative methods appear to be an interesting way for material culture analysis in anthropology and archaeology. Since I want to gradually integrate this big body of knowledge into my archaeological toolbox, I need to know where to begin. What to learn first. I’m curious to know when in an undergraduate program does this type of knowledge is taught?
I might follow one or two undergrad courses, read books and do some online mooc. But what should I learn first? Basic quantitative sociology, social sciences quantitative methods, programing (and which language), statistics, mathematics and graph theory? It seems to me that there is a big learning curve. In the end, I must be kept in mind that I don’t want to turn myself into a mathematician/statistician. I only wish to improve my archaeological researches with quantitative methodology.
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Hi Olivier,
I would also suggest you take a look at Tom Brughmans' website, https://archaeologicalnetworks.wordpress.com/resources/ he has uploaded some really excellent tutorials there which will walk you through some of the different programmes available for doing archaeological network analysis. Also, do join the Google group, the Networks Network, where you will find occasional posts on the topic and you are free to ask any questions to a community of scholars working on the topics. And do come along to any future Connected Past events! https://connectedpast.net/
All best!
Anna
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Hi, basically the question above. I am very interested in ideology & cognition (language) and my interest is in looking at ideology as it moves around on social media. I have come to this point via applied linguistics (cognitive linguistics), where i looked at 'Alt-right' youtube content and the language used and how it is representative of the worlds we mentally construct. Cognitive linguistics is the perfect tool to conduct ideological linguistic analysis (political ideology, 'fake news', propaganda, etc.).
However I am now very interested in using this analysis to look at how ideology 'behaves' on social media (eg twitter). And it is now my understanding that the best way to conduct this analysis would be through Social Network Analysis (and data mining). So, to reiterate, what skills would i need to aquire to be able to conduct SNA (or even just NA)?
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The major skills you need are text mining and programming with scripting languages like python.
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Dear all,
I would like to know if it is currenly possible to use temporal ERGMs (Exponential Random Graph Models) for analyzing weighted networks?
For now, it seems that software packages available to analyse TERGMs (tergm or btergm) only use binary networks.
Thanks in advance for your answer,
Léa Daupagne
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I want to generate some nice prediction plots from my MRQAP model. I've laid out my process below, and would be very grateful to get anyone's insight, as I'm not seeing much written about this online.
I am building my own regression models on network data in R, using quadratic assignment procedure with Decker and colleagues (2007) double-semi-partialling method. In other words, I am predicting the weight of an edge given its respective node traits. This approach uses node permutations of residuals to adjust for interdependence of observations in the network. (Regression with networks involves huge heteroskedasticity, because the observations are literally connected).
Traditionally, this method (MRQAP with DSP) just produces a p-value, and original standard errors are suspect. So, I am using a Doug Altman's method to back-transform p-values into new standard errors that better reflect the actual error range (read more here; thanks to @Andrew Paul McKenzie Pegman: https://www.bmj.com/content/343/bmj.d2090). This at least allows me to make nice dot-and-whisker plots of beta coefficients and with their confidence intervals (estimate + se*.196, etc.). However, I'd still really like to make predictions.
There seem to be two logical routes to make predictions from an MRQAP model.
First, you could just make predictions normally.
This relies on your observed residuals in the model to calculate the standard error for your predictions. I think this might even work, because the homoskedasticity assumption in regression is really about covariate standard error and p-values, not prediction; this means that a heteroskedastic model can still produce solid predictions (see Matthew Drury's & Jesse Lawson's helpful notes here: https://stats.stackexchange.com/questions/303787/using-model-with-heteroskedasticity-for-predictions). However, I would love some external verification on this. Any sources I can draw on to be confident I can use this for visualizing predicted effects from networks?
Second, you could simulate the predictions, like in Zelig/Clarify.
Simulation requires building a multivariate normal distribution, where each vector has a mean of one of your model coefficients, and where the vectors share the same general correlation structure as your variance-covariance matrix. Then, you make a sample from this multi-variate distribution (eg. grab a row of observations from each vector), use these as your coefficients, and generate a set of predictions. You then repeat this about 1000 times, grabbing different sets of slightly-differing coefficients.
In other words, this approach comes with a few assumptions: 1) Your coefficients might be slightly off, but if they're wrong, they follow a normal distribution. 2) The distribution for each coefficient is related to the other coefficients in specific, empirically observed ways. 3) These distributions don't necessarily have standard deviations that reflect the nice new standard deviations generated from our DSP p-values! Ordinarily, I'd think that you'd want a multivariate normal distribution where each assumptions 1 (normal) and 2 (correlated) apply, but where you've also constrained each coefficient's distribution to reflect the standard errors from DSP. But there doesn't seem to be a good way to do this, since standard error doesn't directly factor into making a multivariate normal distribution (to my knowledge). You mostly just need the mean (coefficients) and a variance-covariance matrix.
To any kind souls out there who have read this far, what would you recommend? Should I just use normal prediction? Should I simulate with a multivariate normal distribution? Should I make some weird third multivariate-normal-distribution-that-somehow-resembles-my-standard-errors-made-indirectly-from-MRQAP-DSP?
Any thoughts would be appreciated!
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Thanks Muhammad Ali for your feedback. I'm afraid these papers don't seem to specifically answer how to handle predictions, but I could be wrong. (Dekker and colleagues' 2007 piece is certainly foundational, since they developed the technique I'm using (double-semi partialling). Any thoughts out there would still be very helpful.
Tentatively, for those interested, I've fallen on the following conclusion:
Heteroskedasticity is the big problem in network regression models. But, this is because it inflates type II error for coefficient p-values. Heteroskedasticity does not invalidate model predictions; for example, machine learning models, which are less concerned with coefficient p-values and more with prediction, do not worry about heteroskedasticity as much.
As a result, I have concluded that the standard methods should be fine. Simulations, like used in Zelig, are even better, because the multivariate normal distribution helps us adjust for sampling error too. But, as a safeguard, we probably should only present predictions when varying a coefficient that MRQAP-DSP found to be statistically significant.
Feel free to be in touch if you have thoughts about this; would love to get your input.
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In the times of COVID19 pandemic when most of the academics and research have shifted to online and distance mode, I wonder if there are postdoc positions available without the restriction of being physically present in the lab/ university. Any suggestions or links to open position advertisements can help me in grabbing a postdoc position.
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Hi Sandeep,
I'm looking for remote PostDoc, let us have an online meeting to discuss this further.
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I want to know any viewpoints of information gatekeeping in social network analysis in simple language.
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This term refers to the control of information flowing in a given social network. For example, in an organization two departments work together. In fact, the person who connects the two teams is X- and he/she decides how the information flows and what is communicated to whom. A bit like in chinese wishperers.
I recommend Cross and Parker 2004
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I would like to do a social network analysis based on the forum interactions messages, but I don't know how to build the database for it. Can you help me?
Thank you
Rogério Costa
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Thank you Prof. Ayad Ghany Ismaeel, the paper was very useful.
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I am more interested to know quantitative techniques for assessing social network. IN this regard-
1. is there any existing model/theoretical framework?
2. which is/are appropriate software package to assess the said network?
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If you are interested in analysis of Twitter feed Wayne Williamson’s works could be useful.
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I am looking for a database where social networking sites statistics such as facebook, twitter etc are available for Australian companies. Has anyone an idea whether something like that exists and/or if there is a software which will extract that data? 
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You may try webometric analyst software which can help you track mentions in social media platforms such as YouTube, Facebook, twitter, Mendeley, Altmetrics, Web citations etc. You may download it at https://www.mozdeh.wlv.ac.uk
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Considering researchers that come from qualitative methods background, where is a good place to start? What handbooks/articles/etc would you recommend? Is there interesting online sources to look into? I want to compile some resources for students and colleagues that want to know how to apply SNA.
Thanks!
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Dear Gustavo,
if you have a background with political sciences I would recommend as an introduction:
David Knoke, 1990, Political Networks: The Structural Perspective; Cambridge University Press.
An excellent general introduction is:
Stanley Wasserman and Katherine Faust, 1994, Social Network Analysis: Methods and Applications, Cambridge University Press.
Another excellent intrduction from a more economic perspective:
Ronald Burt, 1992, Structural Holes: The Social Structure of Competition, Harvard University Press.
Kind regards, Volker
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To study the students' interaction patterns, which one is better either social network analysis or epistemic network analysis?
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This time is very difficult time. Under COVID - 19, all the educational Institutions are providing online teaching. Here we have to understand that we should continue our teaching as well as the students should also not stop their study.
This process is required for the welfare of society.
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Recently, I became aware of the social network analysis (SNA). In my mind, the way that SNA works seems quite close to fuzzy cognitive maps (FCM). So, I would appreciate aby feedback on the following:
1) Which are the main differences between SNA and FCM?
2) In which areas/field these techniques are more applicable?
3) Is there any up to date (time is subjective) literature on these topics that relates to environmental/agricultural policy?
Thanks in advance!!!
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1) First of all, in SNA the node represents individual of a given type (instance) and there could (and usually are) be many individuals of that type in the network, while in FCM the node usually represents concept (that is class, not a class instantiation), so there wouldn't make sense to put many of them on one diagram.
The biggest difference, however, is that SNA uses very well (mathematically) defined SNA metrics.
2) You use SNA when conducting research on social set and want to find e.g. individual (one of many others) that performs a specific role in the whole society. E.g. when monitoring bees and relations among them you could easily find the queen and the bee brood. You use FCM when modeling the concepts and their relations to understand more deeply general rules governing a given system (e.g. how the weather conditions - temperature, humidity... influencing the honey production volume).
3) I have no idea :)
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Hello
can we extract some features like "Agreeableness" or "Acting like others" from a public page?
does an introverted person trend to private page?
Is level of Influence equal between introverted & extroverted persons?
Thanks a lot
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I also dont think you can extract such data for public pages!
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E-learning implementation relies on the effective modeling of (among others issues) content, participants-learners, content providers, infrastructural supplies, technology and the organisational culture thereof. These actors do not affect the end result in isolation but as a collective. The effects of each player on the whole can roughly be explained using Activity theory or Actor-network theory but this is not very holistic. I have a feeling that the SNT is better suited to investigate this problem especially that culture has an important role in shaping the effectiveness of e-learning systems.
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Hi everyone,
I am about to design a survey that measures the long-term impacts of social change of a specific scholarship program. I am currently struggling on how to design the questions for the survey, which should both express relational data and measure, from the single student's point of view, the impacts the student thinks to have on his/her society and social networks.
does someone have a clearer idea or examples of previous researches done qualitatively with the SNA?
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Let G = (V,E) is a graph, we have to pick K vital nodes in the network that are capable to spread the information throughout the network. So, how we can define? Either we are going to pick set of key players or K individual players.
Research paper is attached here and link is also given.
  1. Ortiz-Arroyo, D. (2010). Discovering sets of key players in social networks. In Computational social network analysis (pp. 27-47). Springer, London.
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set of k-vital nodes usually means a set comprising of k number of vital nodes. Perhaps K vital nodes refer to the number than the nodes themselves.
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Is there a R package to determine and analyze clusters and subdivision of a network?
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To retrieve tweets :
rtweet
twitteR
R0Auth
==================
To analyse:
tm
ggplot2
tidytext
igraph
ggraph
and so many .....
Regards
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To publish a short communication on pattern recognition, RS, social network analysis, online news etc.
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I am working on Entrepreneurial Ecosystem, where I have to collect the date on the network of entrepreneurs.
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Dear author
please check below science site:
please recommend our response
Thank you very much
Good Luck
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I am referring to a paper on Social Network Analysis. the authors have conducted centrality measures which I was easily able to find in the software UCINET and Socnet.
They have also measured state centrality, link betweenness centrality and out-status centrality which I am unable to find in UCINET 6.
Kindly share any tutorial or mention the steps to find the state centrality, link betweenness centrality and out status centrality.
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I want to convert an unweighted graph to weighted for solving the link prediction problem. Is the best way to transfer from an unweighted graph to a weighted graph to consider the similarity between nodes?
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Not necessarily. It totally depends on your application. If your dataset is on online social networks and you want to model the relation strength among individuals, you could also consider the degree of intimacy, trustworthiness, and influence among individuals. check out these papers:
For the concept of weight on multiplex networks:
Also take a look at Granovetter paper since it is possibly the first paper who defined the concept of weak and strong ties in social networks and modeled them as a set of nodes and links: https://sociology.stanford.edu/sites/g/files/sbiybj9501/f/publications/the_strength_of_weak_ties_and_exch_w-gans.pdf
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Beyond the beautiful lines or webs showing how connected an individual is, what other meaningful analysis could be done? for instance, are those directly connected the the individual more important or useful to those connected through others?
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Social Networks are the most important way to obtain valuable information in marketing, like communities, or influencers. All those interesting aspects may be useful for Community Managers, or for Marketing Campaign design, targetting the products to the right sets of people in the Social Network. Crime detection.
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Hello
I think I can study this behavior with some actions like:
following the trend challenges
posting on Instagram about trend news (to have a reaction about trend news I mean)
What do you think?
would you please inform me about this?
Thanks
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Thank you
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I have already experience with "muxViz" tool and "multinet" package in R. They use some predefined layer like "Fruchterman-Reingold". After plotting the network, I need to change the position and color of a group of nodes, however these tools do not allow such changes. I wondering if there is any library like tkplot in igragh (which is an interactive visualization tool for single-layer networks).
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Thank you Dear David, the paper was really helpful. However, I need to draw a multiplex network, then change the position of nodes based on their community structure (draw the nodes that are in the same community close to each other).
It doesn't need to be 3ِD. Even a multi-graph would be okay. I Attached a multilayer network drawing with muxViz tool. However, this tool doesn't allow for change the position of nodes (it performs some predefined layers definition as input for visualization process).
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I have a number of ongoing researches on adaptive web mining techniques and online social network analysis with applications.
Collaboration with funding support for presentation of research outputs in top conferences, workshops and international journals is highly solicited.
Please you can contact me via temitayo.fagbola@fuoye.edu.ng
Thank you
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I am interested in working on online social networks and social networking analysis
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I have been working with my smartphone for a while and can complete 90% of the tasks without problem. I am really happy as I become very flexible in doing research. However I am not sure if I use an efficient way of doing Social Network Analysis. Can you advise tools (apps, browser based portals etc.) if you have do social network analysis in your smartphone?
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Social networking applications have become an inescapable part of our lives. The power of social networking is such that the number of worldwide monthly active users is expected to reach some 3 billion by 2021. Yes, that’s around a third of the earth’s entire population. Big and popular social networks such as Facebook and Twitter aren’t going anywhere. But people also use niche social networks for different needs. The idea of building new social interactions is great, so it would be useful to learn a bit about the technology stacks that social networks rely on. Because no matter who your target audience is, your social network should offer a great user experience and provide users with core features.
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I am a masters student software engineering, I looking for advice or ideas on how I can find topic related to link prediction or node importance estimation in networks. thanks.
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I think that the new papers trend to use of graph embedding. you can use graph embedding for solving the problem of link prediction.
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For the social Network Analysis, it was hard to find one large organization so I am collecting data from different small organizations in different sectors. I want to combine all of them in one study. Wanted to make sure if there is any technical lacking in my approach, or it would be possible to aggregate the results of all? In that case, How can I interrelate groups from different organizations?
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In SNA, some scholars used parametric statistical tools and more people used non-parametric tools in doing test of ranks, correlations, etc on SNA network descriptives/metrics. Which is the right one to be used for SNA data, the parametric or non-parametric tool?.
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Parametric statistical tests are used based on the assumption that the data obtained were normally distributed while non parametric should be used when the data are not normally distributed. Your choice will depend on any of these assumptions above.
Cheers
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We can see that in social network analysis, marketing, opinion researches sentiment analysis has made serious mark. Web scrapping provides huge data sets and  provides making conclusions and that all is making a new era in statistical analysis.
Is there a place for classical macroeconometrics?
I have seen some text analysis for international business, also I can see that for micro problem you can easily use, let say R's RQDA and that topic modeling can have its place in text analysis of any kind.
Also, I do know that in private sector machine learning is applied for forecasting.
But, yet it seems that academia is rigid in that sense.
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Hi,
I will try to specify the problem.
Machine learning techniques are booming now. Marketing uses advance language processing methods, psychology, and so on.
I now that for commercial purposes there are some great solutions fore forecasting. People use web scrapping techniques and I guess they beside quantitative methods, got use something related to natural language processing.
I can see in this moment intuitively how to use LDA, simple algorithm , to do some topic modeling of some macro problems.
But, I could not find many examples.
Why not do that?
Is there something I cant see?
If someone has found usefull papers related to topic or has some suggestions.....
thanks
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I need to do longitudinal social network analysis, someone recommended SIENA, a package in R. Has anyone used this? Is it easy to use this package in R? What data formats do they need?
Any other suggestions?
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in Russia we use Brand Analytics system (https://br-analytics.ru) to provide social media monitoring
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Hi, fellow researchers! My case is a little bit special. I am doing multiple regression (one dependent variable & several independent variables). My data does NOT have any multilevel structure (e.g., students nested in classrooms, etc.). However, when measuring the dependent variables, measures for different subjects are dependent with each other. Say that Y is the dependent variable, and y is one realization of Y. Then y's are dependent with each other. In order to deal with the dependency among y's, I used permutation test for regression coefficients. I used 'lmp( )' function in lmPerm package. Then based on permutation tests, R calculates sum of squares. So does it make sense to calculate R square based on these sum of squares? Thank you!
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Kelvyn Jones Hi Kelvyn, thank you for your answer! I appreciate your time. For why permutation test can deal with dependency, I think that I need to study more about it. Permutation test can tolerate non-normal distribution for the dependent variable. I assume that permutation test can deal with dependency since in the literature of Social Network Analysis, there is sth called Quadratic Assignment Procedure (QAP). In QAP, it uses permutation test to calculate standard errors. So I simply assumed that permutation test can deal with dependency without further thinking. That might be a mistake. I will study more, and put an answer here when I have a better one.
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Hi,
I'm working on a social network analysis problem.
My goal of the analysis is to predict links in this network.
My network is bipartite and i want to split it into train, validation and test sets ( every set is a network ) in order to check my model efficiency.
what is the best way to split my initial network into train, validation and test networks.
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Do you mean the best dataset split ratio? Or are you talking about techniques to do the splitting?
I'm really new in ML, but if it is about the first case, I started reading about K-fold cross-validation, where you should split the dataset into K folds, then use one as a testset and the union of the remaining as the testset. You do it for all the folds, and get the average of your measurements:
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I am writing my master thesis, which concerns quality soil analysis related to relational goods in small farms. The social relations were analysed throught questionnaires. The questionnaires highlithed all the external relations that a company has. I've used gephi to draw the networks but I do need now some easy-do indicators to formalize what I've depicted. Do you have any suggestions? Also in terms of comparison indicator between biological results (soil quality) and relational goods.
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Social network analysis (SNA) is the process of investigating social structures through the use of networksand graph theory. It characterizes networked structures in terms of nodes (individual actors, people, or things within thenetwork) and the ties, edges, or links (relationships or interactions) that connect them. The development and interest in SNA has increased sharply over the last few decades due to the application of mathematics – notably graph theory and statistical models – and the wide availability of software for network analysis both commercial and freely available through the internet. In addition to the formal, quantitative approach to social network analysis, a qualitative approach to social networks is developing.
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I proposed a comprehensive recommender system for e-commerce usage, but unfortunately i can't find any data-set for evaluation step. I need a data-set containing:
1- Categories
2- Product features (category, price, color, brand, author, RAM and etc. that can be diverse according to the category)
3- User demographic information (age, gender and etc.)
4- User purchase history
5- User browsing history (visiting product's page)
Can anybody help me to find a data-set with this features please?
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Publishing and citing behavior of journals vary across fields. In different fields, different dissemination channel of research activity are preferred, such as in social science books are preferred over journal articles, and in computer science, results are mostly published in conference papers. The number of references per article also vary across disciplines. Similarly, some journals are multidisciplinary, some are open access and some are closed access. Impact factor does not solve the problem of journal comparison across domains. Which metrics and measures or factors could be important in comparing journals in and across the discipline? Reference to any related article will be highly appreciated.
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Hi,
A good source for comparing journals is the Web of Science JCR. It provides a wealth of information on journals, plus graphical presentations.
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I am trying to triangulate the analysis with a social network analysis of specific emergent relationships in NVivo.I do not seem to get clarity from the NVivo guidelines?
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Hi, in addition I was wondering whether anybody had used network analysis in NVIVO 12 and whether there are any differences with NVIVO 11. I know that in 11, you can't manually change the colour or shape of the nodes (or vertices) and you can't change the thickness of the lines. Does anybody know if this changed with the new version?
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I am currently learning how to construct multi-level network in social network analysis. But I have lack of learning materials, references, or online courses for this, especially ones using R programming as analytical tool. Do you have any suggestion?
By the way, I am working on case study in the field of agriculture and the subjects would be the smallholder individual farmers and external actors.
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Overview:
Crossley N, Bellotti E, Edwards G, Everett M, Koskinen J and Tranmer M (2015) Social Network Analysis for Ego-Nets. Sage Publications. [especially Chapter 6].
Multilevel Models for non- or minimal overlapping ego-nets:
Snijders, T., Spreen, M. and Zwaagstra, R. (1995) The Use of Multilevel Modeling for Analysing Personal Networks: Networks of Cocaine Users in an Urban Area, Journal of Quantitative Anthropology 5(2), 85–105.
de Miguel Luken and Tranmer M (2010) Personal Support Networks of Immigrants to Spain: a Multilevel Analysis. Social Networks, 32, no. 4: pages 253-262.
Multilevel Models for social network dependencies:
Tranmer M, Steel D, and Browne W (2014) Multiple Membership Multiple Classification Models for Social Network and Group Dependencies. Journal of the Royal Statistical Society, Series (A), 177, Part 2, pages 1-17.
Multilevel Models overview:
Snijders, T.A.B. and Bosker, R.J. (2012) Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling, 2nd edition, London, Sage Publishers.
R, R2MLwiN, MLwiN web resources:
Quick r website: www.statmethods.net
MLwiN (free to UK based academics) http://www.bristol.ac.uk/cmm/software/mlwin/
Follow this link
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Are there hands-on experts who can support/consult on a telecoms social network analysis on community detection, social ties and relationship mapping?
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An interesting issue regarding the analysis of the development of social media portals.
The problems of the analysis of information contained on social media portals are described in the publication:
I invite you to discussion and cooperation.
Best wishes
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I need your assistance and constructive criticism to a) evaluate parts of my method which are correct b) find weak points and improve on them.
I am far from an expert on ERGM and my case is rather "special" because I am dealing with a large network (most examples that I found were dealing with relatively smaller networks).
I have a network of 7 million edges and 5 million nodes. Nodes have several quantitative attributes. My main goal is to find if these attributes influence the probability of tie formation and if people with similar values tend to have a higher probability to form relations.
Since the network is too large, I took an uniform independent sample for 26697 nodes. The sampling method is favored by literature (see for example http://www.minasgjoka.com/papers/wosn2012-kurant_coarse-topology.pdf). All of their edges even relations to nodes that were not in the sample were included. The sampled network had 39983 nodes and 67024 edges. Then I built a couple of models and I have their results attached to the text file along with the gof of the last one.
I have several questions regarding my results:
1) Do I have to include network metrics (mutual, kstar, etc) if these do not revolve around my hypotheses? Even if I find any results this will probably be irrelevant to the topic that I am working on.
2) I actually did try to build a model for mutual out of curiosity but got back awful diagnostics for mcmc (even with 100,000 sample and 50000 burnin). Instead of normal plots on the right side of the plots printed by mcmc.diagnostics the plots were truly all over the place.
3) The AIC and BIC seem to be quite high compared to other examples. Does it matter? My suspicion is that this is a result of the size of the network.
4) The gof does not seem to fit the data well in several metrics while it is effective in others up to a level. Given the size of the network I am not sure that I will ever get a proper model that would fit the data exactly. Is this however even relevant? Can I still make assertions about my node attributes affecting the probabilities for tie formation?
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Overall, your model did a good job capturing the edge term (first GOF evaluation). Your MCMC diagnostic depicts a model that does not degenerate which is a good news. For the rest of the structure of the network, your model somewhat performs poorly, failing to correctly represent the structure of the observed network. This means that there is still room for improvement regarding how you specify your model.
Depending on the size of the observed network, it is usually difficult to fit a model that correctly reproduce the structure of the observed network. The usual strategy (which I would recommend here) is to fit various model specifications, then choose the best based on the highest log-likelihood (or alternatively the lowest AIC or BIC), and evaluate it using the GOF (including an ROC curve). What you should avoid at all cost is a model that has a good fit but failed the MCMC diagnostic assessment.
I hope this helps!
Thank you,
Roseric
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Complex networks have the number of common features, but is there an outstanding feature of each complex network which does not exist in the rest? For instance, the Rich-club property, there is more to be seen in the brain networks.Can we say each of them has a unique feature?
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Depending on the class of these complex networks. Scale-free networks for example share a common property of having a degree distribution that follows power-law distribution.
Regarding the rich-club coefficient: From my research on social crowdsourcing communities (SCCs), I found that their networks are scale-free and have small Rich-club coefficients, contrary to social networks who are scale-free and have higher rich-club coefficients.
The small rich-club coefficient makes makes sense in SSc because they have moderators who tend to very active and highly popular; yet rarely associate with each other (through following or befriending) or interact with each other in the SCC. This is because of the nature of their duties that makes it useless for them to associate with one another. In my case specifically, moderators are hired by the same SCC so they work off of the same physical space and therefore have direct access to each other off-line, which limits the need for on-line association.
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I am working on mapping advice networks in formal organizations, as a method to voice i.e. a PSU specifically. It is looking at leadership i.e. leadership effectiveness (exchange), informal networks(advice networks) and types of voice.
My questions include 2 scales for the same.
How do I best ask questions for and map advice networks? The roster method is not acceptable to the organization. Will the name generator system with questions on interaction, advice and frequency be effective?
Can you advice me on the best method? Please share your experience and any papers too.
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Fang, R., Landis, B., Zhang, Z., Anderson, M. H., Shaw, J. D., & Kilduff, M. (2015). Integrating personality and social networks: A meta-analysis of personality, network position, and work outcomes in organizations. Organization Science, 26(4), 1243-1260. https://doi.org/10.1287/orsc.2015.0972
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For Social network analysis ?
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Dear Faisal,
If you want to get data for a specific geographical area, you can use Gephi with "Twitter Streaming Importer" plugin.
Best,
İbrahim
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I am getting weird results from my betweenness centrality measure, using a directed, valued data. Its showing the highest betweenness values but on the netdraw they don't seem to be gatekeepers or individuals that connect separate groups?
They do however seem to have higher values in terms of arrow size moving towards them, 
Is it that in directed data, individuals are more likely to pass information to people they like and thats why these off set individuals are scoring higher?
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Consept in the key of Journalism Society
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... or any other SNA problem?
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Good question, When you say "deep learning", you probably mean using "graph embedding" such as node2vec, right ?
Since the publication of the DeepWalk method 4 years ago, this topic is growing very quickly and it is hard to evaluate how good these methods are since 1)new methods are proposed every week basically, claiming to outperform the previous ones, 2)these methods are not well understood, as most of them are based on intuitive application of existing machine learning tools to networks, without much theory.
I tried to answer this question about the link prediction problem in a recent article (https://arxiv.org/pdf/1811.12159.pdf), we also looked with a similar approach to the case of community detection.
The problem in my opinion is that the evaluation of these methods is done in a way that is not very rigorous, at least not in the way things are done in the network science community, because all them try to prove that their method works BEST than the state of the art (that is how things are done in machine learning), and thus do not have a strong incentive to show that other methods work well. So either they do not know well the state of the art in network science, or they deliberately ignore it. For instance, the famous node2vec paper evaluates link prediction by comparing only to unsupervised link prediction using a single feature, which is clearly far from the state of the art. It also uses the AUC only to evaluate the quality, and this metric strongly advantage embedding based methods compared to other ones such as average precision, that give more importance to the first few predictions.
We can say the same for community detection, for instance in the best paper about this topic in my opinion, the article presenting the VERSE method, it is evaluated by trying to optimize the modularity. It is known in the network science community that Mordularity is far from being a good metric of the quality of communities, so the testing approach is not up to date. Nevertheless, the Louvain algorithm outperform all tested embedding approaches on this task.
However, different methods (see GCN, Kipf et al. ...) might have better results when trained for a single task. But they are also harder to train and to tune.
In conclusion, I think that we do not really know how good these methods are currently compared to the state of the art in SNA/Network Science. They are promising, but it would be necessary to make the machine learning community and the SNA/Network Science community talk more together to compare them fairly.
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In the evolving world of social network analysis, with special attention on churn prediction; competitors now engage more in clickbait titles, fake news, and hate speeches with the goal to convince and/or confuse customers or potential customer about their brand(s) for the purpose of customer acquisition and/or retention. While digital media remains the future of any successful business,
1. How can data science, opinion mining & community detection paradigms help customers identify objectivity in the subjective information?
2. What other methods can be engaged to track clickbait titles, fake news, and hate speeches in a social network in real time?
3. What best tool(s) will be more productive in implementing the solutions?
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Gather the about the customer using Social network analysis (SNA). Process that data using Opinion mining.
Your task is not to identify fake news , your task should be how that news is affecting your user. That can be analysed using user's comment towards that news or if user is giving thumbs up to that news.So basically you need to find the user's polarity towards that news and also content polarity with respect to you.
Then use this data as input for machine learning / deep learning models for churn analysis.
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Today, social networks have a special place in people's lives and people spend a lot of time on these networks. These networks have a number of positive and negative effects on the behavior, culture and lifestyle of individuals and society, how can these impacts be managed and to improve society? Is there a technical and scientific solution?
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direct discussion is the best
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  • So many young researchers/scientists even after helping/commitment don’t want to add (≤9) or in rare cases, supervisors/mentors not allowed?
  • Still few researchers delete authors after revising/accepting the paper due to double-digit (>9) authors and vice versa?
  • Why do young researchers behave like this and prefer single-digit (≤ 9) author’s paper?
  • Your opinion matters and will be gratefully appreciated.
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Andrew Paul McKenzie Pegman is correct. I am not aware of addition or deletion of authors after acceptance of MS.
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dears, I am here to ask about the centrality measures in the social network analysis , I am going to build an affiliation network(s) between a set of nodes (directors and boards) to examine the influence on the firm performance , my question is once I get the centrality measures (i,e degree , closeness, between,...etc) , what is the best way to use it in a regression model to be in form of (firm,year) type, and is it valid to use it as it is or I have to contrast a quantile for each measure ?
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You can try to use quadratic assignment procedure-mllregression (QAP-MlR) via UCINET after you make your data set available for analysis.
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How do you feel about disclosing your private info on social media applications such as Facebook? Do you behave the same in your daily life, or is it something special for social media only? How much are you concerned about your privacy in general?
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Unfortunately, contrary to the assurances of companies that run social media portals, the information contained on these websites is not always fully secured by the activities of cybercriminals. In addition, the issue of downloading data from social media portals by large companies to Big Data database systems should be added in order to process them for marketing purposes. The issue of privacy in social media is very important and is related to the security of personal information. Privacy is at risk in terms of information posted on social media portals.
The problems of the analysis of information contained on social media portals for marketing purposes are described in the publication:
I invite you to discussion and cooperation. Greetings
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Hi expert,