Unsupervised Heterophilous Network Embedding via r-Ego Network Discrimination

Recently, supervised network embedding (NE) has emerged as a predominant technique for representing complex systems that take the form of networks, and various downstream node- and network-level tasks have benefited from its remarkable developments. However, unsupervised NE still remains challenging due to the uncertainty in defining a learning objective. In addition, it is still an unexplored research question whether existing NE methods adapt well to heterophilous networks. This paper introduces the first empirical study on the influence of homophily ratio on the performance of existing unsupervised NE methods and reveals their limitations. Inspired by our empirical findings, we design unsupervised NE task as an r-ego network discrimination problem and further develop a SELf-supErvised Network Embedding (Selene) framework for learning useful node representations for both homophilous and heterophilous networks. Specifically, we propose a dual-channel feature embedding mechanism to fuse node attributes and network structure information and leverage a sampling and anonymisation strategy to break the implicit homophily assumption of existing embedding mechanisms. Lastly, we introduce a negative-sample-free SSL objective function to optimise the framework. We conduct extensive experiments and a series of ablation studies on 12 real-world datasets and 20 synthetic networks. Results demonstrate Selene's superior performance and confirm the effectiveness of each component.