Elements of information theory
Elements of information theory
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Compact representations of separable graphs
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
A cross-collection mixture model for comparative text mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Summarizing itemset patterns: a profile-based approach
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Graph summarization with bounded error
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Efficient aggregation for graph summarization
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Neighbor query friendly compression of social networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
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Graph summarization is to obtain a concise representation of a large graph, which is suitable for visualization and analysis. The main idea is to construct a super-graph by grouping similar nodes together. In this paper, we propose a new information-preserving approach for graph summarization, which consists of two parts: a super-graph and a list of probability distribution vectors affiliated to the super-nodes and super-edges. After a carefully analysis of the approximately homogenous grouping, we propose a unified model using information theory to relax all conditions and measure the quality of the summarization. We also develop a new lazy algorithm to compute the exactly homogenous grouping, as well as two algorithms to compute the approximate grouping. We conducted experiments and confirmed that our approaches can efficiently summarize attributed graphs homogeneously and achieve low entropy.