基于图自动编码器重构的快速社区发现
Fast Community Detection based on Graph Autoencoder Reconstruction
摘要：
随着大数据的快速发展，如何在大规模网络中高效、准确地发现紧密的社区结构进行知识发现越来越受到人们的关注。本文首次提出了一种基于图自动编码器重构的社区发现框架。GAER是一个高度可扩展的框架，不需要任何先验信息。我们将基于图自动编码器的一步编码分解为两阶段编码框架，以适应现实世界的大数据系统，将复杂度从原来的O（N^2）降低到O（N）。同时，基于GAER支持模块即插即用配置和增量式社区发现的优势，进一步提出了一种基于对等体感知的实时大图模型模块，该模块能够以更快的速度实现新节点的社区发现，并以6.15倍~ 14.03倍的速度加速模型推理。最后，我们将GAER应用于多个真实世界的数据集，包括一些大型网络。实验结果表明，GAER在几乎所有网络上都取得了优异的性能。
英文摘要：
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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