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6 votes
Accepted

Why an eigenvector might be reasonable notion of centrality

Let the adjacency matrix of our network be $A∈\{0,1\}^{n×n}$ with an empty diagonal ($A_{ii} = 0 ∀i$). Direct approach Let’s start with the approach that a node’s centrality ($C_i$) shall be ...
Wrzlprmft's user avatar
  • 186
5 votes

Finding groups of friends in social network data

Community Detection and Clique Percolation: This is a community detection problem. Here is a very detailed review article surveying the state of the art. The Clique Percolation Method is also ...
Syed Ali Hamza's user avatar
4 votes
Accepted

Clustering Multiple Networks

Your question is not clear in a way there are two different Graph Clustering problems. One is having a dataset of different graphs and you would like to cluster similar graphs (in this case each ...
Kasra Manshaei's user avatar
4 votes

How cluster a twitter data-set?

k-means is very sensitive to noise because it is designed as a least-squares approach. Noise deviations, when squared, become even larger. Twitter is mostly noise Twitter is full of spam and ...
Has QUIT--Anony-Mousse's user avatar
3 votes

Why an eigenvector might be reasonable notion of centrality

Let's define the centrality of a vertex as proportional to the sum of its neighbors' centralities. If you write it out and incorporate the adjacency matrix, the eigendecomposition emerges immediately ...
Emre's user avatar
  • 10.5k
3 votes
Accepted

Twitter Retweet Network Visualization

For answering questions from graph, you should not visualize it. Visualizing graphs is for sake of having an overview on how it looks like in general. There are Graph Visualization techniques that ...
Kasra Manshaei's user avatar
3 votes
Accepted

Name of this type of cross variable interaction Plot

Here is python code to make a pretty good match for your picture. ...
G5W's user avatar
  • 303
3 votes

Is there a metric for "cliquiness" for social graphs?

The formal definition of a clique is a fully connected subgraph (or cliquiness=1.0 in your example) where the shortest path between all the nodes in the clique is 1. To relax that, you have use the n-...
Boyd's user avatar
  • 41
2 votes
Accepted

How can i add weights in a bag of words model in text analysis?

One possible solution is to introduce prior counts for words (higher counts for words that are more important) that could be added to the term-document matrix. An alternative solution is to compute ...
Vadim Smolyakov's user avatar
2 votes

How can I discover topics in a social media data-set?

You can take a look at Latent Dirichlet Allocation. In my experience this does very well without too much effort. You need to remove words that don't help like stopwords (and in your case Twitter ...
Jan van der Vegt's user avatar
2 votes

Why do we do citation analysis?

One simple example of an application of citation analysis is ranking. Imagine you do a keyword search over a collection of scientific articles, How do you rank the result set from most relevant to ...
Dani Mesejo's user avatar
  • 2,226
2 votes
Accepted

Network Analysis using R

Try this R code: ...
knb's user avatar
  • 602
2 votes

In a directed graph, how to measure whether a node is more "upstream" or "downstream"?

Please clarify what you are looking for in the presence of cycles. I'd assume the cycles aren't standalone- any node in a cycle could also have inputs from further upstream, and could also send ...
gms's user avatar
  • 181
2 votes

On Creating an Interoperable Network Matrix

There is no single universally accept standard, however, many packages use edgelist for sparse (un)directed (un)weighted graphs. Regarding storing and sharing, I usually see compressed text files (in ...
faraway's user avatar
  • 21
2 votes

How do I calculate for each person in the network how many people agree with their opinion?

As a fast answer, you can represent each student as a vector with $K$ elements (where $K$ is the number of topics) and values $\{+1, 0, -1\}$, denoting positive/non-existent/negative opinion about ...
Bogas's user avatar
  • 586
2 votes

Building a Citation Network to Analyze in R

I don't understand if you want to build a network of authors who cite each other, or a network of papers who cite other papers (which would be a much sparser network because the coauthorship ...
knb's user avatar
  • 602
2 votes
Accepted

Protein interaction prediction- how to input this data structure

https://en.wikipedia.org/wiki/Adjacency_matrix For such problems, you can tabulate these connections as adjacency matrix and create a network to predict weights for the matrix given some properties ...
Shamit Verma's user avatar
  • 2,259
2 votes

Are social network analysis and graph analytics the same thing?

No, social network analysis and graph analytics are not the same things. Graph analytics are a general set of tools to understand graph structure (e.g., nodes and edges). Social network analysis is ...
Brian Spiering's user avatar
2 votes

Reduce size of a network graph for bipartite projection

I think the problem is not with the data size, but with the presence of large degree nodes (the degree of a node is its number of neighbours). Indeed all neighbours of a node in the bipartite graph ...
Matthieu Latapy's user avatar
2 votes

Method or tool to simulate weighted graphs with a specified weighted degree sequence

Nice question. A natural formalization is as follows. We have a fixed weight vector $b$ (where $b_i$ corresponds to the weighted degree of vertex $i$, that is, the sum of weights of edges incident to ...
Valentas's user avatar
  • 1,229
2 votes

What are good Bipartite Graphs Algorithms?

I can see it as: you want all the edges of graph $G$ while taking the $S \subset G=(V,E)$. For your problem this $S$ is an induced subgraph and the problem you are talking about relates to induced ...
umair mughal's user avatar
1 vote

Building a Citation Network to Analyze in R

Parse an edgelist out of your data, load it into a dataframe with two columns (source, target), then feed that to igraph::graph_from_data_frame
David Marx's user avatar
  • 3,258
1 vote

Identify social users using graph embeddings

This question can have many ways to answer but here is what I think: Lets assume the example of very common, facebook. Very naive sense Number of friends is equal to the 'social-ness' of the person....
mausamsion's user avatar
  • 1,282
1 vote

In what data science applications has the stack exchange dump been used?

This is a Community Wiki answer, everyone is welcome to add references. Kaggle ran a competition to predict whether a question would be closed on Stack Overflow: https://www.kaggle.com/c/predict-...
1 vote

In a directed graph, how to measure whether a node is more "upstream" or "downstream"?

I believe what you are looking are graph centrality measures. The Betweenness centrality for each vertex reflects the ratio of shortest paths going through that vertex: $g(v)=\sum _{{s\neq v\neq t}}{\...
Mortezaaa's user avatar
  • 481
1 vote
Accepted

Visualizing community composition using network of pie charts

Layout algorithms in tools like networkx, igraph, and gephi will associate coordinates with your nodes which you should be able to access fairly easily. Once you have those coordinates, you just need ...
David Marx's user avatar
  • 3,258
1 vote

Can we apply community detection algorithms for word vector space?

"Improving Community Detection in in Wikipedia Articles using Semantic Features" This paper talks about various methods of community detection and might be helpful.
mausamsion's user avatar
  • 1,282
1 vote

how can i collect data set from social networks like instagram?

I am not aware about Instagram, or if there is a ready-to-use dataset for your purposes, but in general in order to get data from (almost) any online source you should a method called "web scraping" (...
quant's user avatar
  • 353
1 vote

Can I use sentiment analysis techniques and text processing for detection social bot in online social networks?

Yes, because it has been done before: http://dl.acm.org/citation.cfm?id=2808779
CalZ's user avatar
  • 1,663
1 vote

How cluster a twitter data-set?

Have you found a good approach? I am envolved in the same work right now. My approach is the following: 1) Make a vector respresentation of all texts in the data set, for example with tfidf technique....
Federico Caccia's user avatar

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