Graph Summarization with Latent Variable Probabilistic Models

Abstract

AbstractThis study addresses the issue of summarizing a static graph, known as graph summarization, effectively and efficiently. The resulting compact graph is referred to as a summary graph. Based on the minimum description length principle (MDL), we propose a novel graph summarization algorithm called the graph summarization with latent variable probabilistic models (GSL) for a static graph. MDL asserts that the best statistical decision strategy is the one that best compresses the data. The key idea of GSL is encoding the original and summary graphs simultaneously using latent variable probabilistic models with two-part coding, that is, first encoding a summary graph, then encoding the original graph given the summary graph. When encoding these graphs, we can use various latent variable probabilistic models. Therefore, we can encode a more complex graph structure than the conventional graph summarization algorithms. We demonstrate the effectiveness of GSL on both synthetic and real datasets.KeywordsGraph summarizationMachine learning and data miningInformation theoryLatent variable probabilistic modelMinimum description length principleNormalized maximum likelihood code-length