Name
Abstract | ||
|---|---|---|
One of the core tasks in social network analysis is to predict the formation of links (i.e. various types of relationships) over time. Previous research has generally represented the social network in the form of a graph and has leveraged topological and semantic measures of similarity between two nodes to evaluate the probability of link formation. Here we introduce a novel local probabilistic graphical model method that can scale to large graphs to estimate the joint co-occurrence probability of two nodes. Such a probability measure captures information that is not captured by either topological measures or measures of semantic similarity, which are the dominant measures used for link prediction. We demonstrate the effectiveness of the co-occurrence probability feature by using it both in isolation and in combination with other topological and semantic features for predicting co-authorship collaborations on three real datasets. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/ICDM.2007.108 | ICDM |
Keywords | Field | DocType |
semantic similarity,probability measure captures information,link prediction,local probabilistic models,leveraged topological,joint co-occurrence probability,link formation,topological measure,semantic feature,co-occurrence probability feature,semantic measure,social network analysis,probability measure,probabilistic model,maximum entropy,social network,data mining | Semantic similarity,Data mining,Graph,Social network,Computer science,Probability measure,Social network analysis,Artificial intelligence,Probabilistic logic,Graphical model,Machine learning | Conference |
ISSN | ISBN | Citations |
1550-4786 | 0-7695-3018-4 | 115 |
PageRank | References | Authors |
6.01 | 13 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chao Wang | 1 | 404 | 27.12 |
| Venu Satuluri | 2 | 423 | 18.82 |
| Srinivasan Parthasarathy | 3 | 4666 | 375.76 |