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How do graph clustering techniques compare with respect to theirsummarization power? How well can they summarize a million-node graph with afew representative structures? Graph clustering or community detectionalgorithms can summarize a graph in terms of coherent and tightly connectedclusters. In this paper, we compare and contrast different techniques: METIS,Louvain, spectral clustering, SlashBurn and KCBC, our proposed k-core-basedclustering method. Unlike prior work that focuses on various measures ofcluster quality, we use vocabulary structures that often appear in real graphsand the Minimum Description Length (MDL) principle to obtain a graph summaryper clustering method. Our main contributions are: (i) Formulation: We proposea summarization-based evaluation of clustering methods. Our method,VOG-OVERLAP, concisely summarizes graphs in terms of their important structureswhich lead to small edge overlap, and large node/edge coverage; (ii) Algorithm:we introduce KCBC, a graph decomposition technique, in the heart of which liesthe k-core algorithm (iii) Evaluation: We compare the summarization power offive clustering techniques on large real graphs, and analyze their compressionperformance, summary statistics and runtimes.

An Empirical Comparison of the Summarization Power of Graph Clustering Methods