Attribute Network Embedding Method based on Joint Clustering of Representation and Network
Authors: 
Wenhan Gao, Peng Wu, Li PanAuthors Info & Claims
Wenhan Gao, Peng Wu, Li PanAuthors Info & ClaimsPages 111 - 119
Abstract
Clustering is the basis of many complex network analysis and application tasks. Preserving the clustering properties in network representation space contributes to a better clustering performance. In this paper, an Attribute Network Embedding method based on Joint Clustering of representation and network (ANEJC) is proposed. Based on variational graph auto-encoder, ANEJC jointly reconstructs adjacency matrix and attribute matrix. In order to preserve the clustering property of the network, ANEJC clusters network structure and hidden layer representations of variational graph auto-encoder, simultaneously. Extensive experiments carried out on four synthetic datasets and three real-world datasets demonstrate a superior clustering performance of ANEJC over the state-of-art methods.
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- Attribute Network Embedding Method based on Joint Clustering of Representation and Network
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Published In
December 2021
133 pages
ISBN:9781450391641
DOI:10.1145/3492324
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Published: 13 January 2022
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- National Key Research and Development Plan in China
- National Natural Science Foundation of China
- Shanghai Sailing Program
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BDCAT '21
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BDCAT '21: 2021 IEEE/ACM 8th International Conference on Big Data Computing, Applications and Technologies
December 6 - 9, 2021
Leicester, United Kingdom
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Overall Acceptance Rate 27 of 93 submissions, 29%
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