Fast Community Detection based on Graph Autoencoder Reconstruction
This addresses the need for scalable community detection in big data systems, though it appears incremental as it builds on existing graph autoencoder methods.
The paper tackles the problem of efficiently and accurately discovering community structures in large-scale networks by proposing GAER, a graph autoencoder reconstruction framework that reduces complexity from O(N^2) to O(N) and achieves 6.15 to 14.03 times faster inference speeds.
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.