Abstract
Techniques to summarize and cluster graphs are important to understand the structure and pattern of large complex networks. State-of-art graph summarization techniques mainly focus on either node attributes or graph topological structure. In this work, we introduce a unified framework based on node attributes and topological structure to support attribute-based summarization. We propose a summarizing method based on virtual links (node attributes) and real links (topological structure) called Greedy Merge (GM) to aggregate similar nodes into k non-overlapping attribute-connected groups. We adopt the Locality Sensitive Hashing (LSH) technique to construct virtual links for high efficiency. Experiments on real datasets indicate that our proposed method GM is both effective and efficient.
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Acknowledgment
This work is partially sponsored by National Natural Science Foundation of China (Grant Nos. 61572365, 61503286), and Science and Technology Commission of Shanghai Municipality (Grant Nos. 14DZ1118700, 15ZR1443000, 15YF1412600). We also thank the reviewers of this paper for their constructive comments on a previous version of this paper.
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Liu, S., Zhao, Q., Li, J., Rao, W. (2017). Graph Summarization Based on Attribute-Connected Network. In: Song, S., Renz, M., Moon, YS. (eds) Web and Big Data. APWeb-WAIM 2017. Lecture Notes in Computer Science(), vol 10612. Springer, Cham. https://doi.org/10.1007/978-3-319-69781-9_16
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DOI: https://doi.org/10.1007/978-3-319-69781-9_16
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