Mining Attribute-structure Correlated Patterns in Large Attributed Graphs

TL;DR

This paper introduces a method for mining dense subgraphs in large attributed graphs that are strongly correlated with specific attribute sets, using statistical significance and efficient algorithms to reveal meaningful patterns.

Contribution

It presents a novel approach combining statistical significance measures with an efficient algorithm for structural correlation pattern mining in large attributed graphs.

Findings

Identifies relevant attribute-structure correlation patterns in real-world graphs.

Provides a scalable algorithm capable of analyzing large attributed networks.

Demonstrates the method's effectiveness on collaboration, music, and citation networks.

Abstract

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.