An Empirical Comparison of the Summarization Power of Graph Clustering Methods
This work addresses the need for better evaluation metrics in graph analysis, though it is incremental as it builds on existing clustering techniques.
The paper tackled the problem of evaluating graph clustering methods based on their ability to summarize large graphs, and found that their proposed KCBC method performed competitively in terms of compression and runtime on real-world datasets.
How do graph clustering techniques compare with respect to their summarization power? How well can they summarize a million-node graph with a few representative structures? Graph clustering or community detection algorithms can summarize a graph in terms of coherent and tightly connected clusters. In this paper, we compare and contrast different techniques: METIS, Louvain, spectral clustering, SlashBurn and KCBC, our proposed k-core-based clustering method. Unlike prior work that focuses on various measures of cluster quality, we use vocabulary structures that often appear in real graphs and the Minimum Description Length (MDL) principle to obtain a graph summary per clustering method. Our main contributions are: (i) Formulation: We propose a summarization-based evaluation of clustering methods. Our method, VOG-OVERLAP, concisely summarizes graphs in terms of their important structures which lead to small edge overlap, and large node/edge coverage; (ii) Algorithm: we introduce KCBC, a graph decomposition technique, in the heart of which lies the k-core algorithm (iii) Evaluation: We compare the summarization power of five clustering techniques on large real graphs, and analyze their compression performance, summary statistics and runtimes.