12

Fancy animations are cool I was very impressed when I saw this animation of the discourse git repository. They used Gourse which is specifically for git. But it may give ideas about how to represent the dynamics of growth. You can create animations with matplotlib This stackoverflow answer seems to point at a python/networkx/matplotlib solution. But D3.js ...


10

Beautiful Soup is specifically designed for web crawling and scraping, but is written for python and not R


6

My first guess is to visualize social network in Tableau. And particularly: building network graphs in Tableau. What you need is to add time dimension to the "Pages" section to be able to see network change dynamics. This is screen from the link above.


6

It turned out that this task was quite easy to accomplish using vis.js. This was the best example code which I have found. The example of what I have built upon this is here (scroll to the bottom of this post). This graph represents the growth of a subnetwork of Facebook friends. Green dots are females, blue ones are males. The darker the colour, the older ...


6

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 proportional to the sum of the centralities of its neighbours with a proportionality constant $\frac{1}{λ}$ (chosen thusly with some foresight): $$ C_i = \frac{1}{λ} \...


5

Tweepy is one of the best libraries for analyzing and hacking around with the Twitter API. (Being a contributor for tweepy, I can vouch for it's stability and quality) For a Python wrapper for the Facebook graph API, you can use the Facebook-Insights library, which is well-maintained and neat documentation. There are services out there which can mine you ...


5

Yes There are! Networkx I think 20k-30k node-edge would be OK on Networkx, IF YOU HAVE A GOOD MACHINE! Networkx is a great library in Python particularly for Graph Analysis so you have access to great analysis tools beside visualizing but visualizing 20k vertices needs much RAM and takes long. Igraph Igraph is another great tool for Graph Analysis with ...


5

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 useful to explore as it pretty much solves what you may need to know. You can also go through what Matching is, and link the concept with Blossom Algorithm. Though, ...


4

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 nonsense tweets. These will be entirely unlike any other and thus have the largest deviations. Chances are you get one "cluster" that contains almost everything, and ...


4

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 object is a graph), and the other when you have a graph (e.g. a social network) and you would like to group similar nodes inside that graph (here each object is a ...


3

Scrapy is a great Python library which can help you scrape different sites faster and make your code structure better. Not all sites can be parsed with classic tools, because they can use dynamic JS content building. For this task it is better to use Selenium (This is a test framework for web sites, but it also a great web scraping tool). There's also a ...


3

Russell's book is fine. You might also like Social Network Analysis for Startups. All the examples are in python. You can do all your analysis in that using packages like networkx. NodeXL is for the Excel crowd. Definitely not the ideal tool for the job; I would shy away from it. The obvious book for NodeXL is Analyzing Social Media Networks with NodeXL, ...


3

I'm going to pursue the following series of online courses on the Coursera: Become a Social Scientist: Methods and Statistics by University of Amsterdam. The good news - it is free, or you can get a nice-looking certificate for $49 or so. The bad news - the nearest enrollment is Aug 31st 2015. You will have opportunity to get a lot of information in ...


3

This question isn't terribly clear. Data analysis and strategic modeling (game theory) are different tasks. Nash equilibrium is a way of understanding the incentives they have by assuming a set of players with assumed utility function and making deductive inferences about what they ought to do to maximize those utility functions given their interaction. ...


3

I think Gephi, an open-source visualization tool, would help you a lot. Actually, as I know, the InMaps and its community detection algorithm are same as the Gephi's.


3

Want to wish you good luck. Some time ago faced with the same problem, but didn't find any satisfying solution. First of all, there is no way to get list of users, who "liked" a particular page. Even, if you are an administrator of this page (I was). One only can get list of last 3 or 5 hundred users. Friendships data for most of the users is also ...


3

The Indico.io API supports Spanish (and Chinese (Mandarin), Japanese, Italian, French, Russian, Arabic, German, English). eg in Python: >>> import indicoio >>> indicoio.config.api_key = <YOUR_API_KEY> >>> indicoio.sentiment("¡Jamás voy a usar esta maldita aplicación! No funciona para nada.") 0.02919392219306888 >>>...


3

Twitter's API is one of the best sources of social network data. You can extract off twitter pretty much everything you can imagine, you just need an account and a developer ID. The documentation is rather big so I will let you navigate it. https://dev.twitter.com/overview/documentation As usual there are wrappers that make your life easier. python-twitter ...


3

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 with the proportional constant as the reciprocal of the eigenvalue. The relevance of the eigenvector is that the centrality is defined through it: the score of ...


3

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 show graph for sake of getting some initial insight (e.g. if there are visually obvious communities or not). Your question has an analytic answer. After ...


3

Here is python code to make a pretty good match for your picture. from igraph import * AM = [[1,3,1,0], [3,7,3,0], [0,1,9,1], [0,1,3,1]] g = Graph.Weighted_Adjacency(AM) g.vs["color"] = ["red", "blue", "blue", "red"] g.vs["size"] = [30,40,40,30] g.es["width"] = [2,5,2,5,9,5,2,13,2,2,5,2] g.es["color"] = ["#FF000066", "#FF000066", "#FF000066", "#...


2

I think ethics in Data Science is important. There is a fundamental difference in using user data to better their experience and show relevant ads and using user data to trick people into clicking on ads for the sake of monetary profit. Personally I like ads that give me relevant information like deals on things I would buy anyway. However, showing me weight ...


2

In graph theory a clique indicates a fully connected set of nodes: as noted here, a p-clique simply indicates a clique comoprised of p nodes. A k-clique is an undirected graph and a number k, and the output is a clique of size k if one exists. Clique Problem


2

The basic thing, you can do in that situation, is to split your query into N simple sentences each of which should be processed in order to receive YES/NO answer considering if the sentence is a query. That way you will receive following results: Input: Gandhi is good guys, Where he was born? -> Gandhi is good guys - not query Where he was born? - query ...


2

I don't think there is a way to build your graph from raw data without using at least basic programming skills. I'm not aware of a drag-and-drop interface for importing and displaying data. Graphs are just a bit too complex. Imagine trying to find the profit of selling a product if all you had was CSVs of receipts dropped into Excel. You'd need labels of the ...


2

The kinds of tools you will use will vary based on the problem you are trying to solve. Social media data is rich and therefore many questions can be asked - and many tools can be used. However, there is a general pattern you might keep in mind. Typically, you will have to use the platform's API to gather data. You will then have to normalized and store the ...


2

Any platform, focused on social networking (not necessarily Twitter), at its core uses the most appropriate and natural abstract data type (ADT) for such domain - a graph data structure. If you use Python, you can check nice NetworkX package, used for "the creation, manipulation, and study of the structure, dynamics, and functions of complex networks". Of ...


2

My favorite place to find information about social network analysis is from SNAP, the Stanford Network Analysis Project. Led by Jure Leskovec, this team of students and professors has built software tools, gathered data sets, and published papers on social network analysis. http://snap.stanford.edu/ The collection of research papers there is outstanding. ...


2

A good introductory course for social network analysis : https://www.coursera.org/course/sna


2

I think Social Media Mining: An Introduction by Zafarani et. al. is an excellent starting point. You can find more about it here. Also a free PDF version is available. It first goes through the essentials in graph theory and data mining. It covers some more advanced topics in graph mining, social network analysis, recommendation systems, etc. Besides, I ...


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