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Clustering analysis has been widely used in trust evaluation on variouscomplex networks such as wireless sensors networks and online social networks.Spectral clustering is one of the most commonly used algorithms forgraph-structured data (networks). However, the conventional spectral clusteringis inherently difficult to work with large-scale networks due to the fact thatit needs computationally expensive matrix manipulations. To deal with largenetworks, in this paper, we propose an efficient graph clustering algorithm,KCoreMotif, specifically for large networks by exploiting k-core decompositionand motifs. The essential idea behind the proposed clustering algorithm is toperform the efficient motif-based spectral clustering algorithm on k-coresubgraphs, rather than on the entire graph. More specifically, (1) we firstconduct the k-core decomposition of the large input network; (2) we thenperform the motif-based spectral clustering for the top k-core subgraphs; (3)we group the remaining vertices in the rest (k-1)-core subgraphs intopreviously found clusters; and finally obtain the desired clusters of the largeinput network. To evaluate the performance of the proposed graph clusteringalgorithm KCoreMotif, we use both the conventional and the motif-based spectralclustering algorithms as the baselines and compare our algorithm with them for18 groups of real-world datasets. Comparative results demonstrate that theproposed graph clustering algorithm is accurate yet efficient for largenetworks, which also means that it can be further used to evaluate theintra-cluster and inter-cluster trusts on large networks.

KCoreMotif: An Efficient Graph Clustering Algorithm for Large Networks by Exploiting k-core Decomposition and Motifs