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Interesting. Sadly it does not mentions any practical tools for doing social network analysis: Here are few that I know: For small networks (up to a million o
by akshayubhat 16y ago
Interesting. Sadly it does not mentions any practical tools for doing social network analysis:
Here are few that I know:
For small networks (up to a million or two million nodes such as Wikipedia Link graph from 2009)
Following libraries provide code to handle and manipulate Network datasets:
1: SNAP by Prof. Jure Leskovec [ http://snap.stanford.edu http://snap.stanford.edu ] written in C++
2: Networkx by Lanl [ http://networkx.lanl.gov/ http://networkx.lanl.gov/ ] written in Python, esp. good for fast prototyping
There are few Databases for storing networks, e.g. Neo4J http://neo4j.org/ http://neo4j.org/ .
Additionally there is a Graph Processing Language called as Gremlin
http://wiki.github.com/tinkerpop/gremlin/ http://wiki.github.com/tinkerpop/gremlin/ .
For Large networks with millions and billions of nodes, one can use Hadoop / Map-Reduce or Apache Hama [still in nascent stage]. Google has a special system known as Pregel which it uses to perform scalable computations over large networks.
- Elite 16y agoI'm watching one of the 2 hours videos from the Stanford professor. He looks like a college freshman! No offense intended. Do you have any resources on how to use these tools (especially the python library), and examples of implementation? I'm interested in learning more and using these tools on real data, but don't necessarily want to spend the time learning all of the theory behind it.
- akshayubhat 16y agoYup he is young, he did a post doc for a year at Cornell [under Prof. Kleinberg] after his PhD, and directly became Asst. Prof at Stanford. Well you can start with reading this book http://www.cs.cornell.edu/home/kleinber/networks-book/ http://www.cs.cornell.edu/home/kleinber/networks-book/ for overview, regarding application of these techniques are considered you can look for papers at recent WWW, NIPS,ICML conferences. The most popular and well studied areas are Link Prediction and Community Detection. The SNAP library comes with some good example code. You can also have a look at Divisi project at MIT Media Lab if you are interested in reasoning/ analogy over the networks. For real datasets, there are a lot of encyclopedic datasets such as Wikipedia / DBPedia /Semantic Web/ Music Brainz, as well as social ones such as Twitter follower network dataset. If you are in a university, you can even get full Web Graph from Yahoo [for research use alone].