Finding Collections of k-Clique Percolated Components in Attributed Graphs
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.
Keywordsgraph mining network analysis attributed graph k-clique percolated component
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