The increasing availability of network data is creating a great potential for knowledge discovery from graph data. In many applications, feature vectors are given in addition to graph data, where nodes represent entities, edges relationships between entities, and feature vectors associated with the nodes represent properties of entities. Often features and edges contain complementary information. In such scenarios the simultaneous use of both data types promises more meaningful and accurate results. Along these lines, we introduce the novel problem of mining cohesive patterns from graphs with feature vectors, which combines the concepts of dense subgraphs and subspace clusters into a very expressive problem definition. A cohesive pattern is a dense and connected subgraph that has homogeneous values in a large enough feature subspace. We argue that this problem definition is natural in identifying small communities in social networks and functional modules in Protein-Protein interaction networks. We present the algorithm CoPaM (Cohesive Pattern Miner), which exploits various pruning strategies to efficiently find all maximal cohesive patterns. Our theoretical analysis proves the correctness of CoPaM, and our experimental evaluation demonstrates its effectiveness and efficiency.
Moser et al. introduced the problem of mining cohesive graph patterns which are defined as dense and connected subgraphs that have homogeneous node attribute values REF .
Mining Cohesive Patterns from Graphs with Feature Vectors
SDM
Computer Science