The worst-case time complexity for generating all maximal cliques and computational experiments
Theoretical Computer Science - Computing and combinatorics
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast best-effort pattern matching in large attributed graphs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
ACM Transactions on Knowledge Discovery from Data (TKDD)
Graph clustering based on structural/attribute similarities
Proceedings of the VLDB Endowment
Properties of Bridge Nodes in Social Networks
ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
Structural correlation pattern mining for large graphs
Proceedings of the Eighth Workshop on Mining and Learning with Graphs
Clustering Large Attributed Graphs: An Efficient Incremental Approach
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Clique percolation method for finding naturally cohesive and overlapping document clusters
ICCPOL'06 Proceedings of the 21st international conference on Computer Processing of Oriental Languages: beyond the orient: the research challenges ahead
Finding itemset-sharing patterns in a large itemset-associated graph
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
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In this paper, we consider graphs where a set of Boolean attributes is associated to each vertex, and we are interested in k -clique percolated components (components made of overlapping cliques) in such graphs. We propose the task of finding the collections of homogeneous k -clique percolated components, where homogeneity means sharing a common set of attributes having value true. A sound and complete algorithm based on subgraph enumeration is proposed. We report experiments on two real databases (a social network of scientific collaborations and a network of gene interactions), showing that the extracted patterns capture meaningful structures.