Ulrik et al Nw Analysis of Collaboration Structure in Wikipedia

This a Paper reviewed for Social Media Analysis 10-802 in Fall 2012.

NOTE - THIS IS A WORK IN PROGRESS!

Citation

Brandes, U.; Kenis, P.; Lerner, J.; and van Raaij, D. 2009. Network analysis of collaboration structure in Wikipedia. In Proc. of WWW 2009.

Online version

Network Analysis of Collaboration Structure in Wikipedia

Summary

This paper describes a graph model to capture properties of individual editors of wikipedia articles and the relationships among them. This model is called the edit network and the paper proposes different attributes for nodes and edges and also describes ways to calculate them using the edit history article.

Using the model described, the paper tries to answer an interesting question of whether there are poles of opinions amongst the authors of a particular article or a topic (set of articles).

They have experimented with wikipedia's List of Controversial Articles and Featured Articles to compare and prove the effectiveness of the bipolarity measures proposed in the paper.

Evaluation

They evaluate their methods by asking following 4 questions :

 - Does NF find out meaningful neighborhoods?
 - How close is Approximate NF to exact NF?
 - Can AD detect injected anomalies?
 - How much time these methods take to run on graphs of varying sizes?

Discussion

This paper poses two important social problems related to bipartite social graphs and explained how those problems can be solved efficiently using random walks.

They also claim that the neighborhoods over nodes can represent personalized clusters depending on different perspectives.

During presentation one of the audiences raised question about is anomaly detection in this paper similar to betweenness of edges defined in Kleinber's text as discussed in Class Meeting for 10-802 01/26/2010. I think they are similar. In the texbook they propose, detecting edges with high betweenness and using them to partition the graph. In this paper they first try to create neighbourhood partitions based on random walk prbabilities and which as a by product gives us nodes and edges with high betweenness value.

Related papers

There has been a lot of work on anomaly detection in graphs.

• The paper by Moonesinghe and Tan ICTAI06 finds the clusters of outlier objects by doing random walk on the weighted graph.
• The paper by Aggarwal SIGMOD 2001 proposes techniques for projecting high dimensional data on lower dimensions to detect outliers.

Study plan

• Article:Bipartite graph:[1]
• Article:Anomaly detection:[2]
• Paper:Topic sensitive pagerank:[3]
  • Paper:The PageRank Citation Ranking: Bringing Order to the Web:[4]
• Paper:Multilevel k-way Partitioning Scheme for Irregular Graphs:[5]