On mining cross-graph quasi-cliques

Abstract

Joint mining of multiple data sets can often discover interesting, novel, and reliable patterns which cannot be obtained solely from any single source. For example, in cross-market customer segmentation, a group of customers who behave similarly in multiple markets should be considered as a more coherent and more reliable cluster than clusters found in a single market. As another example, in bioinformatics, by joint mining of gene expression data and protein interaction data, we can find clusters of genes which show coherent expression patterns and also produce interacting proteins. Such clusters may be potential pathways.In this paper, we investigate a novel data mining problem, mining cross-graph quasi-cliques, which is generalized from several interesting applications such as cross-market customer segmentation and joint mining of gene expression data and protein interaction data. We build a general model for mining cross-graph quasicliques, show why the complete set of cross-graph quasi-cliques cannot be found by previous data mining methods, and study the complexity of the problem. While the problem is difficult, we develop an efficient algorithm, Crochet, which exploits several interesting and effective techniques and heuristics to efficaciously mine cross-graph quasi-cliques. A systematic performance study is reported on both synthetic and real data sets. We demonstrate some interesting and meaningful cross-graph quasi-cliques in bioinformatics. The experimental results also show that algorithm Crochet is efficient and scalable.

Using Semantic Linking to Understand Persons’ Networks Extracted from Text

“…We also plan to improve and extend nodes and cliques classification, for instance, by applying clique percolation (Palla et al, 2005), a method used in Social Media analysis to discover relations between communities (Gregori et al, 2011). Another research direction will deal with almost-cliques (Pei et al, 2005) or node clusters with high (but not maximal) connectivity, so as to increment the coverage of our approach by including more entities. Finally, we would like to exploit the links connecting different Wikipedia biographies to cross-check the information automatically acquired from cliques and investigate whether this can be used to enrich the cliques with person-toperson relations.…”

Mining diversity on social media networks

“…Spectral clustering [16] conducts partitioning based on graph cut theory. In the maximum clique approach, clustering is performed by identifying fully connected subgraphs [19], and extensions have been proposed to overcome this relative stringency by considering quasi cliques and dense subnetworks [7,17]. Recently, RankClus also integrates authority ranking information into the clustering procedure [21].…”

Maximal Biclique Subgraphs and Closed Pattern Pairs of the Adjacency Matrix: A One-to-One Correspondence and Mining Algorithms

“…Indeed, there are plenty of prior work on mining frequent subgraphs [1], [2], [3], [4], [5], [6], on mining patterns from graph databases [7], [8], [9], [10], [11], on mining dense subgraphs or important quasi-cliques crossing graphs [12], [13], on mining closed graph patterns [14], [15] work.) In this paper, we investigate efficient algorithms for mining maximal biclique subgraphs from a large graph.…”