Robust Attributed Network Embedding Preserving Community Information

Network embedding, also known as network repre-sentation, has attracted a surge of attention in data mining and machine learning community as a fundamental tool to treat net-work data. Most existing deep learning-based network embedding approaches focus on reconstructing the pairwise connections of micro-structure, which are easily disturbed by network anomaly or attack. Thus, to address the aforementioned challenge, we pro-pose a novel robust framework for attributed network embedding by preserving Community Information (AnECI). Rather than using pairwise connection-based micro-structure, we try to guide the node embedding by the underlying community structure learned from data itself as an unsupervised learning, as to own stronger anti-interference ability. Specially, we put forward a new modularity function for high-order proximity and overlapped community to guide the network embedding of an attributed graph encoder. We conducted extensive experiments on node classification, anomaly detection and community detection tasks on real benchmark data sets, and the results show that AnECI is superior to the state-of-art attributed network embedding methods.