Node Embedding Preserving Graph Summarization

Author:

Zhou Houquan1ORCID,Liu Shenghua1ORCID,Shen Huawei1ORCID,Cheng Xueqi1ORCID

Affiliation:

1. CAS Key Laboratory of AI Security, Institute of Computing Technology, Chinese Academy of Sciences, Haidian District, China, and University of Chinese Academy of Sciences, Huairou District, China

Abstract

Graph summarization is a useful tool for analyzing large-scale graphs. Some works tried to preserve original node embeddings encoding rich structural information of nodes on the summary graph. However, their algorithms are designed heuristically and not theoretically guaranteed. In this article, we theoretically study the problem of preserving node embeddings on summary graph. We prove that three matrix-factorization-based node embedding methods of the original graph can be approximated by that of the summary graph, and we propose a novel graph summarization method, named HCSumm , based on this analysis. Extensive experiments are performed on real-world datasets to evaluate the effectiveness of our proposed method. The experimental results show that our method outperforms the state-of-the-art methods in preserving node embeddings.

Publisher

Association for Computing Machinery (ACM)

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5. Chenhui Deng, Zhiqiang Zhao, Yongyu Wang, Zhiru Zhang, and Zhuo Feng. 2020. GraphZoom: A multi-level spectral approach for accurate and scalable graph embedding. In Proceedings of the ICLR.

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