Science topic

# Network Analysis - Science topic

Explore the latest questions and answers in Network Analysis, and find Network Analysis experts.

Questions related to Network Analysis

The pandemics radar seems to be fragmented and compartmentalized, but a lot of risk clustering seems to be going on with ML applications.

Is there a holistic pandemics risk framework on the horizon with synthesis networks of emerging correlations/causations?

I'm curious can it be possible to study ego-networks of a person/organization/bank/city etc. via using ERGM and SAOM? Which one is better for making a research? Can you recomend some papers on it?

Furthermore, how effective are the current prevention mechanisms in mitigating these threats?

Searching the most important texts in Buddhism/ Hinduism describing the concept of relatedness to something - contrasting dualistic thought.

Happy for links/research.

what are the best libraries (in python) or programs to perform network analysis of MD simulation trajectory for discovering allosteric effects?

thanks

**Dear Friends and connection**

I believe in the power of community. So, I post this,

I am excited to explore the possibility of collaborating with someone who works on network pharmacology. As, network pharmacology is an interdisciplinary field that combines principles of network analysis, bioinformatics, and pharmacology to investigate drug-target interactions and predict the therapeutic effects of drugs.

I have some projects related to bioinformatics and I believe that our collaboration can result in significant progress in this exciting field.

I am looking forward to hearing from you and exploring our collaboration for network pharmacology.

**Regards**

Shopnil Akash

WhatsApp: +8801935567417

Email: shopnil.ph@gmail.com

Hello,

I applied exploratory factor analysis with network analysis to data from healthy and diseased patients. The analysis shows different clusters of parameters; some are similar in both groups, and some groups cluster differently. For instance, the parameters IleValLeu are similarly clustered in both groups whereas the parameters Pro Ala are not (see figure).

How shall I interpret the data? For instance, in the Pro/Ala case, I expected to see some differences between the groups, but they look pretty much the same to me.

Is the differences about correlation? The scatterplot of the data shows slightly different regression models, but not something compelling.

Is it the value itself? But again there is no real difference in the value distribution between the two groups.

So, what is the actual outcome of the network analysis?

Thank you

During my Master's in Archaeology program, I explored network analysis at a beginner level. I am eager to delve deeper into this field for testing innovative methods and theories. I am looking forward to a PhD.

My focus is not solely on

*social*network analysis, but rather the innovative applications of the broad concept of network in archaeological reasoning. My background is in prehistoric archaeology in Northeastern America, but I am open to different cultural areas and historical periods. I have specific ideas/questions suitable for methodological research.Can you recommend professors, universities, and research groups?

In street analysis with axial lines, the lengths of axial lines do not correspond to street length. with this in mind, I look for more recent space syntax research that may instead relate to segments and/or road center lines but how does one determine the street lengths in this case, if a road or street is broken into segments?

Given the network clusters in the hierarchical systems of the 14th and early 15th centuries, what impact had the Black Death on the relative shifts in institutional structures and power zones in Continental Europe, e.g. Hanse, and knight networks especially the demise of the Deutsche Orden?

Who profited from the shifts?

Hi everyone,

Im quite new to co-expression network analysis and I've been wondering what would be the minimum time points I could use to create a meaningful co-expression network to analyze? I currently have 2 time points with 8 samples each but after reading around it seems 3 might be the minimum? Is that true, do I need more than 3, or could I extract meaningful information from 2 timepoints? On a similar note is there any publication I might be missing where this is stated? Thank you!

Typically, interaction network analysis uses binary data (absence/presence) or abundance data (frequency) to analyze the network structure (nested or modular). When the abundance data comes from direct observations of the interactions, it makes sense to use them instead of just the binary data because the interaction frequency is biologically important. When one obtains information about the partners involved in an interaction from NGS (for example, fungi associated with plant roots in mycorrhizal interactions), it is usual that some fungi appear more frequently than others (that is, there is a higher frequency of readings of some sequences than others). Does it make sense to use the number of reads as abundance data to build the networks and evaluate their structure, or would it be more prudent to simply use binary data?

With most enterprise resources being hosted on the cloud, users tend not to be very aware of online security etiquette. This leaves the organisation's resources vulnerable to adversary attacks.

Who should be responsible for the total all-round awareness, implementation and enforcement of resource security? Is it the service providers, the clients or both?

Hello Everyone,

I have one task provided by our prof. We want to write down extended abstract on semantic network analysis. I am unable to understand how can i narrow down my topic for making posters and extended abstracts for this topic. If any one has experience in this topic please share your thoughts.

Thanks in advance.

Regards

Ashish Kumar

The R function

*centralityPlot*plots by default variables in alphabetical order, but I am considering to sort them according to their domain.I already used the

*label*option assigning abbreviations (i.e. NC1, NC2, SC1, SC2,...) and got the variables belonging to the same domain close to each other, but in this way the single constructs are not immediately recognizable. So a user-defined sorting would solve my problem, is it possible?thanks a lot

I have microbial networks for more than 2 groups(6). I want to analysis the community structure and network properties among these networks all at the same time. Any web based or R-based tools would be helpful to me. Additionally I am also looking forward for tools that can be used to analyze the longitudinal nature of microbiome data and provide the networks or other attributes of microbiome. Any help and suggestions are welcome. Thanks

I would like to design a microgrid based on an IEEE bus system for the purpose of validation of a certain technique. Should the MG have the same standard loads of the IEEE bus system ?

Can I design the MG (say based on 14 bus system) and have only few Kilowatts as a load (50 KW load) , does this make sense or it deems impractical?

Can someone explain me what are the common layer 3 problems that happen in a network? specially in SOHO environment if possible.

I preprocessed resting state fMRI by GRETNA toolbox to do graph theory analysis. After preprocessing step completion, when I saw the normalization quality check for data I found the following problem (see attached figure). I checked to compare the EPI image before pre-processing and figure after normalization, before and after figures are the same cut from above.
So I thought that problem is not related to normalization or other steps of the preprocessing. Am I right? I am new to this area. I want to know the reason for this error and should I exclude this subject from my network analysis?

Hi, RG community! I am new to network analysis and I am currently facing a challenge with coding, processing, and quantifying networks in a hierarchical scheme. In this scheme, nodes pertain to differing hierarchical ranks and ranks denote inclusion relationships. So, for example if node “A” includes node “Z”, it is said that “A” is “Z”’s parent and “Z” is “A”’ daughter. However, a rather uncommon feature is that nodes at different ranks of the hierarchy can relate in a non-inclusive fashion. For example, node “A” parent of “Z” may have a directional link to “Y”, which is “B”’s daughter (if “A” were directionally linked to “B”, then it could be said that “A” is “Y”’ aunt). Here is a more concrete example to illustrate the plausibility of this scheme: “A” is a website in which person “Z” is signed in (inclusiveness; specifically, parentship); website “A” can advertise banners of website “B” (siblingship) or recommend to follow a link to person “Y” profile in website “B” (auntship).

OK. So, in the image below (left top panel) I present a graphical depiction of this rationale. For simplicity, a two-rank hierarchy is used, where gray and red colors denote higher and lower hierarchies, respectively. The image displays siblingship, parentship, and auntship links. My first approach to coding this network scheme was to denote inclusiveness as one-directional relationships (green numbers) and simple links as symmetrical (two-way; brown numbers) relationships (see table in right panel). However, I soon realized that this does not reflect what I expected in networks’ metrics. For example, I am mainly interested in quantifying cohesiveness and the way I coded the network in left top panel entails something like the non-hierarchical network depicted at the left bottom panel. In short, I am not interested in the directionality of the links but in actual inclusiveness. To my mind, the network in the top panel is more cohesive than that in the bottom panel but my coding approach does not allow me to distinguish between them formally.

The solution conceived in the interest of solving this problem was to stipulate that a relationship between any pair of nodes implicates a relationship of each with all of the other’s descendance. This certainly yields, for example, the top network being more cohesive than that in the bottom, which is in line with my goals. However, this solution is not at all as elegant as I would have hoped. Can anyone tell if there is a better solution? Maybe another way to code or an R package allowing for qualitatively distinct relationships (not just one-way or two-way). Thank you.

I have Scopus data of articles. I want to do a citation network analysis s to detect the structure of the community of citation networks in the past literature. I have seen many tutorials. But could not do it in Gephi software. Can someone help me do it in Gephi or any other software?

Hello to all,

I have previously performed gene expression analysis using R to find hub genes that were significantly different in expression from control genes, I mean differentially expressed genes (DEGs). In some articles I red about weighted correlation network analysis (WGCNA) that its concepts is very similar to those of finding DEGS.

I want to know if anyone has ever done WGCNA in R?

Does it have a special command?

Do I have to install a specific DEGs package? Or Is this analysis different from the gene expression analysis that determines the DEGs ?

I am very grateful for the guidance of experts in this field.

Hello

I want to camparing two Psychiatric groups in their connections .

But, because of our financial limitation , we need the minimum sample size.

Hi guys, I am looking for someone who understands the R language and is willing to be a partner in the production of a scientific paper. More specifically in creating a thematic mapping, multiple correspondence analysis (MCA), the k-means clustering, and network analysis of historical citations.

Hello everyone!

I am currently dealing with some transcriptomic data and building protein-protein interaction (PPI) networks. After filtering my data by fold-change and p-value, I got quite a lot disconnected nodes in my PPI network. So I would like to expand my current network through a whole-genome network (as a template) in order to connect the maximum number of single nodes. The main assumption is that not all proteins being at play in a biologic response will show a change in their transcript level, and that up-regulated proteins may interact with partners (yet present, and unmodified during the biological response). My goal, thus, is to connect the maximum amount of query nodes with the minimum amount of newly added nodes.

The STRING database has an option of "adding more nodes to the current network", but it usually enriches current clusters rather than connecting single nodes (or at least it seems to me). However I don't know what strategy does STRING follow to choose nodes to be added. So, what would be the best network expansion strategy to connect single nodes?

Thanks in advanced!

I'm reading a study which contains the following quotation:

"in many cases there may be limits to the number of relations that any one point in the network can sustain (Scott, 1991: 77). Where this is the case, the actual number of lines possible in any graph will be limited by the number of points in the graph and therefore, all other things being equal, larger graphs will have lower densities".

Can anyone provide me with a further reference or two to back up the idea that larger networks have lower densities?

Hello everyone,

I am searching for a road network dataset and its corresponded traffic data. I have found some road network dataset in the web, however, no traffic data has been attached to it. Therefore, I would be grateful if anyone could help me in finding such data.

Thank you in advance.

The problem of self-interaction effects and errors arises in studies of, for example, anions, electrons, atoms and molecules.

It also arises in developing a theory of network effects in connection with network entropy (for example, https://arxiv.org/abs/0803.1443 ). In the network case, the concept of degrees of freedom leads to an apparent resolution.

Does the network case generalize?

Dear colleagues,

I am looking for software for Systems mapping and Causal loop diagrams. It would be wonderful if any of you with experience in this could share some information.

Have you used any? Any advice or feedback? Pros and cons?

Thanks in advance,

Fabio

I am new to Organizational Network Analysis and my lecturer told me to seek for a software that does ONA, whether the software is free of charge or not it does not matter. Thank you in advance.

Hi everyone! I'm working on a network analysis project and have some questions about how to calculate and interpret eigenvector centralization in signed network. Hoping someone can help me out.

Considering a signed network with symmetric adjacency matrix like this:

0 -0.5 0.85

-0.5 0 -0.43

0.85 -0.43 0

and its eigenvector centralities of each node are {0.63, -0.48, 0.61}

Here are my questions:

1. How can I calculate the eigenvector centralization of such network using Freeman's method (with the maximum centrality comparing to every centralities; Freeman, 1978)? I found most papers use this method only in unsigned network.

2. How to interpret the centralization I calculated? I have tried to use standard deviation and gini coeffienct to calculate the eigenvector centralization. But I realized that is problematic. For example, even change the sign of centrality above to {-0.63, 0.48, -0.61}, I will still receive the same centralization. In other words, I can't differentiate whether the network is positively or negatively influenced in general.

I'll be more than grateful if anyone could give me some instructions to my question!

Best regards!

Hello. I am trying to run a haplotype analysis in PopArt. It's going well until I realized I can not load a previous work in PopArt. I can only export the graphical output as .svg, .png, or .pdf but not as a "network" file which I can reload or edit if I want to in the future. I noticed that it can be saved as a .nex file and the new file actually had additional lines (the portion of the code started with: "Begin NETWORK"). I think this is supposed to be read by PopArt but it fails to do so. I encounter parsing errors when I try to run the new file. I am not sure if there is a way around this as I am new to the software. Any help would be appreciated. Stay safe, anon!

I simulated a protein structure for 50ns, I conducted residue interaction energy analysis on the equilibrated last 1ns of the trajectory. Using gRINN, I calculated the pairwise interaction energy of each residue. Now I wish to find out clusters in the protein structure and calculate their total interaction energy ( for each cluster).

Any suggestion will be appreciated. Thanks in advance

I have a research-related question to how can I easily read my results off a Co-occurrence network from VOS Viewer. Please provide any links to articles I can relate to.

Hi,

I am looking for some article/book/clip about performing and interpreting Network Analysis using R in psychology studies. I would be grateful if you could help me in this matter.

Thank you

I am trying to construct a haplotype network with over 400 mitogenomes with each one of length 15 kbp. I get the notification of "inferring the network" which disappears in a minute and after that the plot area is still blank with no network drawn whatsoever. There is no way to know whether the POPART is still calculating/drawing the network or is just stuck.

Anyone else with same problem? is there any solution?

I want to find the resonance and anti-resonance frequencies of an ultrasonic transducer by analyzing its impedance.

so I need to buy a impedance analyzer or spectrum analyzer or something like that.

but my budget is limited.

do you recommend any device for my application and limited budget? :D

Hello everyone! For my dissertation I am using Network Analysis to model my data. I have 11 variables and all but 3 of them are likert scales. I am struggling to test for linearity for my data (linearity is an assumption for network analysis). Obviously when I am trying to test linearity using standardised regressions (ZRED against ZRESID) the scatterplot is not homoscedastic because of the likert scales. Is anyone familiar with Network analysis assumption testing regarding likert-type data??? any help appreciated :) My data is not normally distributed however I am using npn transformations (in JASP) to solve this issue for the networks. Just don't know how to test for linearity as relations among variables need to be assumed to be linear.

I am using SPSS for data cleaning etc. and JASP to run the network.

Dear experts and colleagues,

Hello, all! I recently received a reviewer's comment stating that the proposed network measures of graph theoretical analyses could be correlated by mathematical definition.

So I ran the correlational analyses and global efficiency, characteristic path length, mean clustering coefficient (global measures) showed correlated to each other.

I understand that Global efficiency is inversely related to characteristic path length, but I am quite confused about how characteristic path length is inversely correlated with mean clustering coefficient, while mean clustering coefficient is positively correlated with global efficiency.

Or does my data is wrongly suggested?

Briefly explaining my study, it is a neurological study using MR images (diffusion tensor imaging) to explore the structural networks.

Any feedback and discussion are welcomed here!

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:

- Should I test each team network individually or club them to get organizational correlations? Should be there any other partition applied?
- 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?

hello,

im trying to build an IoT dataset for attack detection for my master thesis , the dataset should contain normal and anomaly data,

im using Wireshark to capture the network packets and CICflowmeter to extract the features from the pcap file and generate the CSV, now my problem is how to label each instance of this dataset, is there any script, program, algorithm or equations i could use to label each instance as normal or as udp flood attack .....

The similarity here refers to the similarity between two networks instead of two nodes in the same network. Node sets of the two networks are not completely different nor same. Besides, the two networks are un-directed, weighed network. How can I measure the similarity between these two networks, any algorithms or tools?

It seems there are some biological tools to compare certain biological networks. My networks are not biological. However, if a biological tool could measure the similarity between non-biological networks, please let me know.

Thank you.

I am trying to come up with a code in R to perform network and dynamic network DEA. I would really appreciate if someone knows any available R package that can help me out.

I want to model the interlinkages between several dimensions using

network analysis. I never used this technique but I just read about it in the relevant literature.

I found Gephi software which seems to be user-friendly but my question is how can I model panel data using the software ( my model include variables such as GDP). is it suitable for such analysis?

many thanks,

I have a directed graph that ends at point "L". When I sum all the incoming degrees at point "L", I got the value: 13. But I want to modify the graph based on the given plot (see the attached figure). In the graph, the incoming degree will be distributed in the following nodes. For instance, the incoming degree of point "F" is 2 therefore the value for "F" is 2. But in the case of point "K" and "G", the value will be 0.5 and 0.5 because the value of "H" is distributed into two parts. The value of point "J" will be the sum of the incoming values in the upper nodes (i.e., G, E, F).

When I study about inductors I found that inductors have a constant value and value of inductance depends upon the permeability of core. And when I see the hysteresis curve then found that permeability depends on magnetising current and initial state of core(if it has retained some magnetism earlier). So I think inductor is non-linear in pure sense but approximated to linear. And in capacitors also due to analogous nature we can predict that it also nonlinear and capacitance is also not fixed due to variable permittivity.

The article, Simoiu, C., Sumanth, C., Mysore, A., & Goel, S. (2019). Studying the "Wisdom of Crowds" at Scale. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 7(1), 171-179. Retrieved from https://ojs.aaai.org/index.php/HCOMP/article/view/5271 notes that it is (p. 172):

“... one of the most comprehensive studies of the wisdom -of -crowds effect to date ..”

Are there any other comparable studies? If so, can you please provide the citations?

A different approach is to use emergent collectively solved problem sets, such as the English lexicon or rates measuring increases in efficiency for emergent collectively solved problem sets, such as :

1) The rate of improvement in domestic lighting: Nordhaus, W. D. (1994). Do real-output and real-wage measures capture reality? The history of lighting suggests not. Technical Report 1078, Cowles Foundation for Research in Economics, Yale University;

2) The rate of increase in average IQs. A theory of intelligence.arXiv:0909.0173v8;

3) By assuming a general collective problem solving rate, and finding a formula connecting the collective rate with the average individual rate of problem solving:

Preprint The rate of thought

I have a question about "network score" of IPA Network analysis. In many papers, the top 5 networks were listed in tables, while in these tables some network scores are high (around 50), but others are low (less than 20). We use the same method for network analyses, and got the impression that we can see tight association between genes when "the network score" is higher than 40. However, we have not found literature discussing the meaningful "network score" (we found one paper described that “the networks are selected if their score is higher than 21”). We would appreciate it if you could let us know information about such a meaningful network score or your impression/experience of the network score (for example, did you see tight association of genes when the network score was less than 20?).

I have 50 survey responses coded as 0 and 1 in a square matrix format pasted on different excel worksheets. I want measure the frequency on 0 and 1 in each cell to identify if 0 is having frequency or 1. What is probable method for it??

Can someone provide me with the

**R code for Network Analysis**where I can establish the relationships among the variables using the underlying concepts of SNA using the data from Social Media. I have been going through the codes on Stackflow and GitHub but the machine time and processing time is very high when working on data extracted from Social Media.*Your help will be acknowledged by mentioning your name in the published manuscript on this work.*

I´d like to test whether a priori defined edges and communities change across two severity groups of inpatients with dissociation. We seperated groups via subthreshold and matched in ratio 1:1 for age and gender to the sample of high dissociators. But how to deal with the fact that less variance can be explained in patients with low dissociation. Can we adjust matching ratio to 1:2?

happy for any help,

Philipp Göbel

#networkanalysis #networkcomparison #symptomicsframework #severitygroups

Hi everyone,

I have a zone of water distribution system with a couple of reservoirs. I am going to manage the whole zone's valves using an Artificial Intelligence based system. I have an idea that if we can find some critical points in the zone which are very pressure sensitive about valves' changes, we can manage the whole zone's valves just based on providing the suitable pressure of these critical points. The rest points of zone would have suitable pressure because the AI model supplies the pressure of sensitive (critical) points. In the other words, the critical points would have potential for losing pressure or bursting the related pipes in the worst cases (low pressure and high pressure respectively).

However, I do not know how I can find these critical points based on hydraulic network and data mining techniques.

Do you believe that this idea would be meaningful and practical?

If yes, would you please give me some practical ideas about detecting these critical points?

Thanks in advanced

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

I need to find TTL from Python and I found several codes but I don't know why TTL output in python different than TTL when I used ping www.google.com?

for example in python TTL = 45 or 226 ...but in command line terminal ping www.google.com TTL =118.

Do you know any python code that I obtain TTL which is matched with TTL in the command line?

Dear Researchers,

`Since I am new in the field of internet security, I need your suggestion regarding the meaning of the following features.

We have DNS google.com or youtube.com, and so on, and I want to extract different features based on Lexical and Web Scrapped.

Lexical Features:

what is the meaning of the following features? Please write with an example.

1) different ratios (different ratios (number to length, alphabet to length) ?

2) hash?

3) distance between a number to an alphabet? (You can find the meaning of these features in the paper Feature Extraction Approach to Unearth Domain Generating Algorithms (DGAs) - Page 401)

4) English domain name, not English yet pronounceable domain names, uni-gram?

Web Scrapping:

we extract information of the queried domain name from the web using Python (You can find the meaning of these features in the paper Feature Extraction Approach to Unearth Domain Generating Algorithms (DGAs) - Page 403)).

1) Levenshtein distance (sq1,se2), what is seq2?

2) Typosquat process?

Thanks

Hy! I analyzed data using geo databases and filtered having specific p value and log f/c values. Now I need to analyse data of Mirna and lncRNA on wgcna but I don't know how to construct network.

I want to find motif in my PPI network but i don't know which of the cystoscope's plug-in is better .Anyone has experience with that or know any other motif detector ?

thanks

Hi everyone,

I am looking for some advice on what to use to perform a network analysis. Specifically, I want to evaluate the frequency/strength of connections between country collaboration on pan-European projects (i.e. Erasmus+ Projects). I already have the data set in Excel, but I do not know how to translate that to a proper network analysis or into a graph depicting these relationships.

So I am putting the question out there: what software would best and most easily allow me to do so?

Thanks!

Louis

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

I am planning to compile a systematic literature review. I have downloaded .csv file of a collection of papers and their respective bibliometric information (authors' names and affiliations, year of publication, keywords, references, etc.). To conduct a network analyses of this acquired data in Gephi, I need to import different spreadsheets for making nodes and edges tables. The .csv file downloaded may be used as it is for nodes (if I'm not wrong). However, I am clueless about how to make edges file from this parent .csv file to conduct different types of network analyses (e.g., based on keywords, citations, research areas, etc.). Any guidance in this regard will be highly appreciated.

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!

Some excerpts from the article

Comparing methods for comparing networks Scientific Reports volume 9, Article number: 17557 (2019)

By Mattia Tantardini, Francesca Ieva, Lucia Tajoli & Carlo Piccard

are:

*To effectively compare networks, we need to move to inexact graph matching, i.e., define a real-valued distance which, as a minimal requirement, has the property of converging to zero as the networks approach isomorphism.*

*we expect that whatever distance we use, it should tend to zero when the perturbations tend to zero*

*the diameter distance, which remains zero on a broad range of perturbations for most network models, thus proving inadequate as a network distance*

*Virtually all methods demonstrated a fairly good behaviour under perturbation tests (the diameter distance being the only exception), in the sense that all distances tend to zero as the similarity of the networks increases.*

If achieving thermodynamic efficiency is the benchmark criterion for all kinds of networks, then their topologies should converge to the same model. If they all converge to the same model when optimally efficient, does that cast doubt on topology as a way to evaluate and differentiate networks?

I've been working on the network analysis of adolescent depressive symptoms. I've estimated the network, the centrality indices and network stability using packages qgraph and bootnet. Then I want to detect if there're communities or subnetworks of symptoms within the whole network. I know from the previous research that I should use package igraph. However, any function in igraph require a graph object which I don't know how to create with my raw data.

Can the network or the plot created by qgraph or bootnet transformed into the graph that igraph requires?

I am working on a project using network analysis to identify core symptoms in depression among Chinese underprivileged adolescents.

I conducted a network comparison test between boys and girls and the results for

**network invariance test**showed the global structure isn’t invariant (p=0.018). I would like to do an**edge invariance test**and I just didn’t know how to interpret the results.Hi all,

Can anybody suggest a "quick-and-dirty" way to summarise a measure of connectivity between polygons, based on a network of line? See the picture as an example, say that I have some districts connected by a network of roads (black lines). The red one is contiguous with both the yellow and the grey. But it has only 1 road connecting with the yellow, and 5 with the grey. Is there a clever way of summarising this? Maybe accounting also for the lenght of the roads?

Thanks in advance.

Luca

**I want to consider the effect of layers similarity to solve a problem in multilayer networks. Can anyone help me which are the most important interlayer similarities in multilayer networks?**

Dear peers,

I am working with extinction models in interaction networks using the bipartite R-package.

I can determine the order in which species will be removed based on abundance and degree or random.

For example:

#####

data(Safariland)

abundance <- second.extinct(Safariland, participant = "lower", method = "abundance", nrep = 10,

details = FALSE, ext.row=NULL, ext.col=NULL)

robustness(abundance)

slope.bipartite(abundance)

#####

However, I need and am not able to elaborate a vector to determine a different extinction sequence, using the ‘external’ method.

Thanks

The primary focus is to perform a miRNA-miRNA synergistic network analysis, with the goal to predict / identify miRNA pairs that might have a high probability of regulating a pathway or a set of pathways in a miRNA-miRNA network based on shared target genes.

It would be great if you can suggest some good resources to get started with network analysis, and significance of networks in biology with context to miRNA networks.

Thanks!

What are the methods used to assess the robustness of network with flow? How to model the flow (discrete entities with origin-destination routes) in a network.

He is interested in collaborating in a multicultural project of psychometric network models on the multidimensional concept of the light triad (humanism, faith in humanity and Kantianism) and dark personality traits, to date we have collaborators from Brazil, Poland, Peru, Nigeria and Colombia. The first multi-country study is presented as evidence (DOI: 10.2139/ssrn.4347559), and several similar cross-cultural projects are being developed simultaneously with other mental health and personality concepts (if you accept your participation you can consult the OSF for the most current network research). Some of the work being done on these personality concepts also includes data from South Africa, Turkey, Slovakia, United Kingdom, El Salvador and the United States. Therefore, we invite interested researchers who can survey in their respective countries, who will co-author SCOPUS Q1 articles with the contribution of their respective surveys (minimum 400 participants per country).

Study mentioned

My profile demonstrates correlational, comparative and longitudinal network studies with new methodological contributions.