Patrick Mackey added an answer:Social network question: Is there a technical term for an ego network in which the central node is removed?
I've seen networks of this type used to highlight communities within your list of friends on Facebook. It removes yourself from the network, because this node is by default connected to everyone and clutters the visualization. Looking to see if there is a standard technical term for such a network.
Nope, not necessarily. It's often still connected.Following
Martin Schulz added an answer:Social media: Downvoting and degrading in social networks – What are the reasons? - What are the reactions?We have recently seen a lot of down-votings in some threads. We also saw different kinds of reactions on this phenomenon. I would like to ask a question about these reactions.
First I assume that there are several possible reasons for downvotings: dissent – misunderstandings – misuse of buttons without knowing it– a social scientist who writes a paper about the reactions – a test carried out by RG – some technical problem, etc.
I would like to have a discussion about the reactions on the part of researchers. We have seen calls to ban down-voters or to cancel their anonymity. Elections in democracies are anonymous and there are good reasons for this. I think most of us agree about some basic traits of democracy, the right to stay anonymous is among them. What about these basics in social media?
Nicholas, your post gave me this idea: there are things that are to be banned without any assessment of the pros and cons, such as a Gulag. No question of being fair in relation to the item or proposing an amelioration of it. That was my point. It must be possible to be against something without having to give arguments, e.g. how we would run a better Gulag.
And this holds true for other things that are far more harmless, like theories! Many theories were abandoned without any amelioration.Following
Jarret Cassaniti added an answer:Is anyone working on Ebola contact-tracing with mobile phones?
Is anyone working on Contact Tracing, in the current Ebola outbreaks, using non-interview (non-F2F) techniques (such as tracking mobile phones) to gather information on movements/interactions of infectious individuals?
Hi Valdis, you can check the Ebola Communication Network: http://ebolacommunicationnetwork.org/latest-materials/. You can also post your question on the Springboard for Health Communication Professionals: http://www.healthcomspringboard.org/groups/ebola-communication/Following
Ra'ed (Moh'd Taisir) Masa'deh added an answer:Where can I find dataset for Optimization Queue for Scheduling Tasks in Cloud Computing ?
My work requires the Real-world data sets for arrival of tasks and task service time.
Could you please see our paper on Job Scheduling for Cloud Computing Using Neural Networks. It might help. Yours,Following
Giulio Costantini added an answer:What are the applications of network analysis?
There is basically no limit to the phenomena that can be modeled and analyzed in terms of complex networks - entities and their relationships between which can be represented as the nodes and edges of a graph, and which form a non-trivial pattern. So let's make this a small survey:
- Where in your research do you employ complex networks and network analysis methods?
- What are your data sources? How big are they?
- Which tools do you use for the network analysis process?
- What did you learn from the network analysis?
Nice idea for a small survey, here are my answers.
Where in your research do you employ complex networks and network analysis methods?
Research in personality psychology.
What are your data sources? How big are they?
In my research, nodes represent personality characteristics (of different levels of abstraction) and edges represent their pairwise relationships. These variables are usually collected by administering questionnaires to a few hundreds of participants up to a thousand. The number of nodes is usually not large (a few tens to one hundred), but we may investigate larger networks in the future.
Which tools do you use for the network analysis process?
Usually R, in particular packages qgraph, igraph, sna, parcor have been particularly useful.
What did you learn from the network analysis?
It's a great theoretical and methodological perspective in my field and we just started adopting it. As an example, we are investigating ways to find which are the most relevant personality characteristics, those that are expected to produce greatest overall changes when manipulated.
Here are a few papers that can give the idea of what we do.Following
Wu-Chen Su added an answer:What are the current active research areas in social network analysis?I'm trying to choose my MSc research area in the field of social network analysis but I'm new to this subject and am not aware of currently active research areas or the trends. I would appreciate any suggestion you might give, so I can choose the one closest to my interests.
You may consider my paper for current issues and trends of multiple online social network study to find suitable topics for your study.Following
Gandhi Kishan Bipinchandra added an answer:Are there any good visualizations showing the turbulence of real-life scale free networks?
I am looking for good (dynamic - i.e. dynamic gif or video) visualizations that show how turbulent real-life international scale-free networks can be. For instance, the global propagation of computer viruses; of 'viral' memes, etc. Can anybody help me?
have a look it
Maurizio Campolo added an answer:Which software are you using for complex network analysis?There is a variety of software packages which provide graph algorithms and network analysis capabilities. As a developer of network analysis algorithms and software, I wonder which tools are most popular with researchers working on real-world data. What are your requirements with respect to usability, scalability etc.? What are the desired features? Are there analysis tasks which you would like to do but are beyond the capabilities of your current tools?
I apply Brain Connectivity Toolbox (BCT, http://www.brain-connectivity-toolbox.net/ source C++, Matlab and Octave) contains a large selection of complex network measures, statistic and comparation, by Sporn/Rubinov.
I use another source: http://strategic.mit.edu/downloads.php?page=matlab_networks .Following
Abdulmunem Khudhair added an answer:Book RecommendationsCan anybody recomend me a book about networks in general?
Thanks a lot
Go for Cisco v5Following
Ritesh Kumar added an answer:Is there any method for finding optimal number of communities using network based community detection methods such as louvain method?
Graph based community detection methods are very effective in explaining the underlying structure of graph but i have not come across any method find optimal number of community similar to clustering methods.
I am sorry, I should have made it a bit clear.
Say, I am trying to identify the communities in an unsupervised manner and for that I am trying to maximize the modularity. Now, I get different number of communities with different nodes even at a single resolution parameter. The question arises which of the communities is the best i.e. is there any statistical criteria which can lead me to find that number?
Moreover, the choice of resolution parameter itself is a question mark.Following
Valdis Krebs added an answer:What are some of the best models describing the epidemic spread over a network?Epidemic in Networks
Looking for some of the best papers or thesis to go through.
The spread of TB/HIV in human networks...
Follow links to original papers with the CDC at bottom of article.Following
James R Knaub added an answer:In cluster sampling method, On what basis we calculate the number of clusters to be selected?
Please help me
The number of clusters, I would think, would have to be a compromise between the difficulty in traveling to or otherwise reaching the clusters in the first stage, and the number of smallest units you can handle for a sample size. Cluster sampling, unlike stratification, actually increases the overall sample size needed, but may lower your cost. Also, in general, the larger your sample size, the greater might be your nonsampling error, like measurement error, but the convenience of cluster sampling (which is a randomization/design-based method) may ameliorate this to a degree.
So to determine the number of clusters depends upon the convenience (NOT "convenience sampling") of this design, the sample size you can handle, and the accuracy you can attain considering both sampling error and nonsampling error. So this is rather customized.
You could look into this in a book such as Cochran, W.G.(1977), Sampling Techniques, 3rd ed., John Wiley & Sons.
Cheers - JimFollowing
Ingo Vogt added an answer:Tools for analyzing large scale networksWhat kind of tool would you suggest for the analysis of large scale biological networks? The tools I have used so far are Cytoscape and Network Workbench. Both have some really nice features but also disadvantages. I would like to discuss with others about this topic and if there are recommendations for specific types of networks making a tool advantageous compared to others?Following
Valdis Krebs added an answer:Is general sparsification of complex networks possible?
Nowadays complex network data sets are reaching enormous sizes, so that their analysis challenges algorithms, software and hardware. One possible approach to this is to reduce the amount of data while preserving important information. I'm specifically interested in methods that, given a complex network as input, filter out a significant fraction of the edges while preserving structural properties such as degree distribution, components, clustering coefficients, community structure and centrality. Another term used in this context is "backbone", a subset of important edges that represent the network structure.
There are methods to sparsify/filter/sample edges and preserve a specific property. But are there any methods that aim to preserve a large set of diverse properties?
Let's assume you have a large human network such as cell-phones or Facebook users and your network is > 1M nodes. This network gives the FALSE impression that anyone is connected to anyone else, by some path, in the network. But most FB and cell-phone activity happens in local clusters (those 1 and 2 steps away from you). So rather than look at 1 large component of 1M nodes it makes more sense to look at the natural clusters of dozens/hundreds of people who really know each other (or at least of each other). So how do you reduce the large component to hundreds/thousands of real life social circles? A human network of > 1000 nodes usually makes no sense from a sociological perspective -- because most people are strangers in large networks.
1) Often the reason we end up with one very large component is that we have set the bar TOO LOW for what an edge is. If you have edge strength/frequency to work with, move it up to get rid of the noisey connections while retaining at least "weak ties".
2) Many networks have many satellites/hangers on with a degree of 1... hide all of those (you get one or more 2-cores). This will start to show the natural emergent clusters in the data. If you still have one large component you may try a 3-core or 4-core... but no more.
3) Betweenness is normally calculated using all geodesics in a component. Yet, it can also be calculated using just the shorter geodesics (maxing out at 3 steps). This efficiently find the spanners between clusters. There iterative removal will leave you with hundreds of natural social circles. Keep the high betweenness nodes you removed as a separate network -- as the backbone.
4) Investigate the smaller clusters and the backbone to understand both local behavior and global structure.Following
Abhishek Dwaraki added an answer:Is an Erdos-Renyi Communication network possible?We know very well that a communication network is always assumed to be or also exhibits the nature of Scale Free Networks. But is it possible that a communication network can be framed as ER Network or a Random Network?
Links to any papers or thesis that model communication networks as ER or Random model would help too.I think this might shed some useful information on whether networks can be modeled on the Erdos-Renyi model and can be scale free or not. It is basically a review paper, but has some interesting points that can be made use of.Following
Henning Meyerhenke added an answer:Datasets of networks for benchmarking community detection algorithmsIs someone knows where to find datasets of networks with known communities (that's the important point), in order to have reference clusters to validate/invalidate community detection algorithms ?Following
Alex Shackelford added an answer:Can anybody please name some common applications of Complex Networks (aka. network science) preferably related (but not limited) to computer science?I'm trying to write a report and I need some hot topics/applications to emphasize. Does anybody also know about the particularly active area of research in Complex Networks?Im not really sure what you mean by complex networks, but Ive been working for Internet service providers for the past year, and I know if you figured out how to network neighborhoods of people together, where they would efficiently share bandwidth, I think that would be very useful.Following
Manikant Prasad added an answer:SIR or SIS model, which one is more accurate for explaining the spread of computer virus in a network?In SIS/SIRS model the network components which are infected are assumed to recover and go to susceptible state based immediately or after some time based on immunity of the virus.
While in SIR model, once recovered, the nodes are assumed to have become immune to the same disease and no longer participates in the spread of epidemic.
I was just wondering which model describes the computer virus epidemic more accurately.Thanks sir for your answer :)Following
Peteris Daugulis added an answer:What are current algorithmic challenges in connectome analysis?The human connectome is a comprehensive map of the neural connections in the brain - in other words, a graph. Coming from a background in graph algorithm development and network analysis, the field of connectome analysis seems to me a very interesting application domain. However, it is a domain I am just beginning to understand. Therefore I hope to get some feedback from both neuro- and computer scientists, starting with the following questions:
- It is my understanding that at the neural scale, the connectome is a graph of more than 10^10 nodes and 10^14 edges. If it could be comprehensively mapped at this scale - which i believe it cannot at this point due to a lack of imaging technology - would it be in the range of current computing capabilities to analyse such a network?
- Has the connectome been mapped at coarser scales? If yes, what graph sizes are we talking about?
- Are standard measures from network analysis (such as degree distribution, diameter, clustering coefficients, centrality, communities) relevant for connectome analysis? What are interpretations of such measures?
- What are other structures of interest in the connectome that could be revealed by graph algorithms? Is there a need for domain-specific algorithms to discover brain-specific graph structures?
- Are there publicly available datasets that represent the connectome as a graph?About the need for previous expert knowledge - not necessarily so. For example, you don't need expert knowledge to find a new motif, just search for motifs.Following
Ergys Rexhepi added an answer:Is there any simulator for home networks?I know NS and GloMoSim for Ad-hoc networks. Is there a simulator used for home networks?Try GNS3 if you have a powerful computer (except for catalyst switches) or you can perform everything you want with Packet Tracer, free cisco product.Following
Deepankar Mitra added an answer:Why not take advantage of electrical signals to charge mobile devices from the open air?As long as the air carries electrical signals, why not take advantage of these electrical signals to charge mobile devices, "charging in case of emergency," for example.I guess you must have heard about "witricity" or wireless electricity. But the problem with it is, it can't be deployed everywhere. It needs a proper set up. Check out this Wiki link-
and this one also:
Mehdi Hedayatpour added an answer:What is the importance of ecological memory to anthropogenic disturbance?Ecological memory (EM) is an important and relatively new concept in ecology. How can we apply our understanding of EM to anthropogenic disturbances? Do anthropogenic disturbances alter EM in some systems? Is EM erased by some types of anthropogenic disturbance? Can we design anthropogenic disturbances to optimize EM?Precipitation, soil water, and other factors affect plant and ecosystem processes at multiple time scales. A common assumption is that water availability at a given time directly affects processes at that time. Recent work, especially in pulse-driven, semiarid systems, shows that antecedent water availability, averaged over several days to a couple weeks, can be just as or more important than current water status. Precipitation patterns of previous seasons or past years can also impact plant and ecosystem functioning in many systems. However, we lack an analytical framework for quantifying the importance of and time-scale over which past conditions affect current processes. This study explores the ecological memory of a variety of plant and ecosystem processes. We use memory as a metaphor to describe the time-scale over which antecedent conditions affect the current process. Existing approaches for incorporating antecedent effects arbitrarily select the antecedent integration period (e.g., the past 2 weeks) and the relative importance of past conditions (e.g., assign equal or linearly decreasing weights to past events). In contrast, we utilize a hierarchical Bayesian approach to integrate field data with process-based models, yielding posterior distributions for model parameters, including the duration of the ecological memory (integration period) and the relative importance of past events (weights) to this memory. We apply our approach to data spanning diverse temporal scales and four semiarid sites in the western US: leaf-level stomatal conductance (gs, sub-hourly scale), soil respiration (Rs, hourly to daily scale), and net primary productivity (NPP) and tree-ring widths (annual scale). For gs, antecedent factors (daily rainfall and temperature, hourly vapor pressure deficit) and current soil water explained up to 72% of the variation in gs in the Chihuahuan Desert, with a memory of 10 hours for a grass and 4 days for a shrub. Antecedent factors (past soil water, temperature, photosynthesis rates) explained 73-80% of the variation in sub-daily and daily Rs. Rs beneath shrubs had a moisture and temperature memory of a few weeks, while Rs in open space and beneath grasses had a memory of 6 weeks. For pinyon pine ring widths, the current and previous year accounted for 85% of the precipitation memory; for the current year, precipitation received between February and June was most important. A similar result emerged for NPP in the short grass steppe. In both sites, tree growth and NPP had a memory of 3 years such that precipitation received >3 years ago had little influence. Understanding ecosystem dynamics requires knowledge of the temporal scales over which environmental factors influence ecological processes, and our approach to quantifying ecological memory provides a means to identify underlying mechanisms.Following
Moon 14 added an answer:Evaluation matrix for new MAC protocol in wireless sensor networksWhat performance metrics are to be measured for the new MAC protocol for wireless sensor networks with multi-rate sensor nodes?
Especially I focus on proportional fairness between nodes.Thanks a lot
What about throughput?Following
Christopher Landauer added an answer:How fast does knowledge grow in a network?In a directed network of agents passing knowledge to each other how fast does information in a node grow with the number of edges incident on it? Linearly or exponentially with the number of edges? If on one hand the knowledge of the node agent seems to grow with the sum of the knowledge shared through the incoming edges, on the other hand each item of information shared through one edge might recombine with each item of other edges incoming information, to form new items of information. For example if agent A tells me that there is a traffic jam near the shopping center and agent B tells me that today morning there are big sales there, I get to acquire a third peace of information from combining what agent A and Agent B told me, which is that the traffic jam is caused by the sales and won't stop until the sails are over. Any ideas on which model better suits reality? Sorry if this sounds as a rather naïve question, but since it is related, but not central to my research, I did not get to do a literature search on it.a while ago, i wrote some papers on computational semiotics that are at peripherally relevant. The ``Get Stuck'' Theorems show (1) that no matter what finite representational mechanism you use, if you are trying to represent more and more complex phenomena, you will get stuck (the size of the description exceeds your computational capacity to compute with it), and second, that even if humans are adding to a base of knowledge, eventually the knowledge will become too cumbersome to change, mainly because there will be too many interconnections
Cognitive Technology: Instruments of Mind: 4th International Conference, CT ...
edited by Meurig Beynon, Chrystopher L. Nehaniv, Kerstin Dautenhahn, Springer LNAI 2117 (2001)
the relevance is that a faster increase in knowledge can lead to getting stuck sooner, so it isn't always the most desirable behaviorFollowing
Sedat Acar asked a question:Wrong arrow direction in Gephi?I am using buyer-supplier data. Gephi is reading and visualising a firm's data inversely. This mistake affects the results. How can I solve this problem?Following
Steve George added an answer:Updating publications onlineCan anyone recommend a site for documenting my research online so that it will be visible to all viewers?If Researchgate is the best then the others must be bl**dy awful. As far as I can work out it's impossible to see a list without the abstracts without turning them off INDIVIDUALLY! I know my publication list is missing about fifty entries, but it's impossible to view it to see which ones. Totally user-unfriendly.Following
Muhammad Riaz added an answer:What is the expected impact of agile organisations on enterprise architecture?enterprise architecture can itself be made agile by optimising the organisation it represents, to make it agile. Businessmen however, expect something from this process. ROI? organisational effectiveness? resilinece to change?Agile organisations are adaptive and responsive to changes in the business environment. Agile organisation will tend to improve the enterprise architecture based on the customer feedback and needs. As change is way of life for an agile organisation, Organisational efficiency and effectiveness, and resilience are incorporated in the enterprise architecture.Following
Horst Fickenscher added an answer:Is there a simulator for evaluating a social network?I want to evaluate the performance of a social network. I want to know whether there exists a simulator for evaluating the performance of a social network. What are the performance parameters and how are they evaluated?Following
Max Jonas Werner added an answer:Antivirus And Internet securityDifference between Antivirus and Internet security - which one is most effective in detecting any type of viruses.Following
Chintan Amrit added an answer:Is there an online tool to evaluate and analyze a social network?I want to know whether there an online tool exists other than a simulator to evaluate and analyze the realtime performance of a social network?There is a nice compilation of SNA tools here: http://en.wikipedia.org/wiki/Social_network_analysis_software
Just search for "browser" - to get the online tools ( I think there are 4 mentioned in the list)Following
About Network Science
Physical, engineered, information, biological, cognitive, semantic and social network research.