Difference between revisions of "Sun et. al., ICDM 2005"
| (4 intermediate revisions by one other user not shown) | |||
| Line 3: | Line 3: | ||
== Citation == | == Citation == | ||
| − | + | Neighborhood Formation and Anomaly Detection in Bipartite Graphs, | |
| − | + | Jimeng Sun, Huiming Qu, Deepayan Chakrabarti, Christos Faloutsos, ICDM 2005 | |
== Online version == | == Online version == | ||
| − | [http:// | + | [http://www.cs.cmu.edu/~deepay/mywww/papers/icdm05.pdf Neighborhood Formation and Anomaly Detection in Bipartite Graphs] |
| − | ] | ||
== Summary == | == Summary == | ||
| − | This paper | + | This paper poses two interesting social problems on [[bipartite graph]] named [[AddressesProblem::Neighborhood formation and Anomaly detection]]. They also propose solutions based on [[UsesMethod::Random walk with restart]]. |
| − | + | The experimented on 3 real world social graphs [[UsesDataset::Conference-Author dataset]], [[UsesDataset::Author-Paper dataset]] and [[UsesDataset::IMDB dataset]] | |
| − | + | == 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 == | == Related papers == | ||
Latest revision as of 08:38, 27 September 2012
This a Paper discussed in Social Media Analysis 10-802 in Spring 2010.
Contents
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
This paper poses two interesting social problems on bipartite graph named Neighborhood formation and Anomaly detection. They also propose solutions based on Random walk with restart.
The experimented on 3 real world social graphs Conference-Author dataset, Author-Paper dataset and IMDB dataset
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.
