Mining attribute-structure correlated patterns in large attributed graphs

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.

Exceptional contextual subgraph mining

“…Similar to that work, Günnemann et al (2010) present a method based on subspace clustering and dense subgraph mining to extract non redundant subgraphs that are homogeneous with respect to vertex attributes. Silva et al (2012) extract pairs of dense subgraphs and Boolean attribute sets such that the Boolean attributes are strongly associated with the dense subgraphs. Similarly, Mougel et al (2013) introduce the problem of mining maximal homogeneous clique sets.…”

Cohesive Co-evolution Patterns in Dynamic Attributed Graphs

“…Moser et al [16] pioneered this topic proposing a method to find dense homogeneous subgraphs (i.e., whose vertices share a large set of attributes). Silva et al [22] extract pairs of dense subgraphs and boolean attribute sets such that the attributes are strongly associated with the subgraphs. The authors of [17] introduce the task of finding collections of homogeneous k-clique percolated components (i.e., made of overlapping cliques sharing a common set of attributes).…”

“…As a result, graph mining has become an extremely active research domain. It has recently been extended into several complementary directions as multidimensional graphs [5], attributed graphs [16,17,22], and dynamic graphs [6]. Indeed, entities can be described by one or more attributes that constitute the attribute vectors associated with the graph vertices.…”

“…Recent contributions have shown that using additional information associated to vertices enables to exploit both the graph structure and local vertex attributes [16,17,22]. Dynamic graphs have been studied in two different ways.…”

Trend Mining in Dynamic Attributed Graphs

“…With the rapid development of social media, sensor technologies and bioinformatic assay tools, real-world graph data has become ubiquitous and new dedicated data mining techniques have been developed. Whereas dynamic graphs [2,4,13,15] and attributed graphs [12,14,16] have been separately considered so-far, we focus on the extraction of valuable information from dynamic attributed graphs. The simultaneous consideration of the graph structure, the vertex attributes and their evolution through time makes possible to tackle a wide variety of mining problems.…”