Fast Community Detection based on Graph Autoencoder Reconstruction

• URL: http://arxiv.org/abs/2203.03151v1
• Date: Mon, 7 Mar 2022 06:04:27 GMT
• Title: Fast Community Detection based on Graph Autoencoder Reconstruction
• Authors: Chenyang Qiu, Zhaoci Huang, Wenzhe Xu, Huijia Li
• Abstract summary: We propose a community detection framework based on Graph AutoEncoder Reconstruction (noted as GAER) We decompose the graph autoencoder-based one-step encoding into the two-stage encoding framework to adapt to the real-world big data system. We further propose a peer awareness based module for real-time large graphs, which can realize the new nodes community detection at a faster speed.
• Score: 0.4129225533930965
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the rapid development of big data, how to efficiently and accurately discover tight community structures in large-scale networks for knowledge discovery has attracted more and more attention. In this paper, a community detection framework based on Graph AutoEncoder Reconstruction (noted as GAER) is proposed for the first time. GAER is a highly scalable framework which does not require any prior information. We decompose the graph autoencoder-based one-step encoding into the two-stage encoding framework to adapt to the real-world big data system by reducing complexity from the original O(N^2) to O(N). At the same time, based on the advantages of GAER support module plug-and-play configuration and incremental community detection, we further propose a peer awareness based module for real-time large graphs, which can realize the new nodes community detection at a faster speed, and accelerate model inference with the 6.15 times - 14.03 times speed. Finally, we apply the GAER on multiple real-world datasets, including some large-scale networks. The experimental result verified that GAER has achieved the superior performance on almost all networks.

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