论文标题
基于图形自动编码器重建的快速社区检测
Fast Community Detection based on Graph Autoencoder Reconstruction
论文作者
论文摘要
随着大数据的快速发展，如何在大规模网络中有效，准确地发现知识发现的紧密社区结构吸引了越来越多的关注。在本文中，首次提出了基于图形自动编码器重建（称为Gaer）的社区检测框架。 Gaer是一个高度可扩展的框架，不需要任何事先信息。我们将基于图形自动编码器的图形单步编码分解为两个阶段编码框架，以通过将复杂性从原始O（N^2）降低到O（n）来适应现实世界中的大数据系统。同时，基于GAER支持模块的插件配置和增量社区检测的优势，我们进一步提出了一个基于同伴意识的实时大图的基于同伴意识的模块，该模块可以以更快的速度实现新的节点社区检测，并以6.15次-14.03倍的速度加速模型。最后，我们将GAER应用于多个现实世界数据集，包括一些大规模网络。实验结果验证了Gaer在几乎所有网络上都取得了出色的性能。
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.
