In this work, we study the correlation between attribute sets and the occurrence of dense subgraphs in large attributed graphs, a task we call structural correlation pattern mining. A structural correlation pattern is a dense subgraph induced by a particular attribute set. Existing methods are not able to extract relevant knowledge regarding how vertex attributes interact with dense subgraphs. Structural correlation pattern mining combines aspects of frequent itemset and quasi-clique mining problems. We propose statistical significance measures that compare the structural correlation of attribute sets against their expected values using null models. Moreover, we evaluate the interestingness of structural correlation patterns in terms of size and density. An efficient algorithm that combines search and pruning strategies in the identification of the most relevant structural correlation patterns is presented. We apply our method for the analysis of three real-world attributed graphs: a collaboration, a music, and a citation network, verifying that it provides valuable knowledge in a feasible time.

No abstract

Abstract. How are personal interests related to the communities in large social networks? In order to answer this kind of question, we introduce structural correlation pattern mining, which is the identification of interesting associations between vertex attributes and dense subgraphs. We present a model and algorithms that explore search, pruning, sampling and parallelization strategies to solve this problem for large graphs. Results show that structural correlation pattern mining enables the discovery of relevant patterns in real-life datasets.

No abstract

No abstract