Node Embedding for Network Community Detection



Node Embedding for Network Community Detection

Node Embedding for Network Community Detection
Research Paper / 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) / Mar 2017 / Machine/Deep Learning/AI

Neural node embedding has been recently developed as a powerful representation for supervised tasks with graph data. We leverage this recent advance and propose a novel approach for unsupervised community discovery in graphs. Through extensive experimental studies on simulated and real-world data, we demonstrate consistent improvement of the proposed approach over the current state-of-the-arts. Specifically, our approach empirically attains the information theoretic limits under the benchmark Stochastic Block Models and exhibits better stability and accuracy over the best known algorithms in the community recovery limits.