A general framework for relation graph clustering
Relation graphs, in which multi-type (or single type) nodes are related to each other, frequently arise in many important applications, such as Web mining, information retrieval, bioinformatics, and epidemiology. In this study, We propose a general framework for clustering on relation graphs. Under this framework, we derive a family of clustering algorithms including both hard and soft versions, which are capable of learning cluster patterns from relation graphs with various structures and statistical properties. A number of classic approaches on special cases of relation graphs, such as traditional graphs with singly-type nodes and bi-type relation graphs with two types of nodes, can be viewed as special cases of the proposed framework. The theoretic analysis and experiments demonstrate the great potential and effectiveness of the proposed framework and algorithm.
relational graph clustering heterogeneous links homogeneous links bregman divergences