Mark my words!

This a Paper discussed in Social Media Analysis 10-802 in Spring 2010.

Citation

Neighborhood Formation and Anomaly Detection in Bipartite Graphs, Jimeng Sun, Huiming Qu, Deepayan Chakrabarti, Christos Faloutsos, ICDM 2005

Online version

Neighborhood Formation and Anomaly Detection in Bipartite Graphs

Summary

Physiological studies have suggested that participants in conversation accommodate in dimensions such as style, utterance length, gesture, speaking rate etc. In this paper authors investigate accommodation in twitter. They propose a novel probabilistic framework to compute measures such as stylistic cohesion,stylistic accommodation and stylistic influence and symmetry.

Authors use non-topical LIWC[link] dimensions to compute measures mentioned above. Some of examples for these dimensions include use of Article,Negation words(not/no),Preposition,Quantifier,1st person singular pronoun,1st person plural pronoun,2nd person pronoun in conversation.

Evaluation

They evaluate their methods by asking following 4 questions :

 - Does NF find out meaningful neighborhoods?
 - How close ispproximate 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]