Unsupervised graph representation learning (UGRL) has drawn increasing
research attention and achieved promising results in several graph analytic
tasks. Relying on the homophily assumption, existing UGRL methods tend to
smooth the learned node representations along all edges, ignoring the existence
of heterophilic edges that connect nodes with distinct attributes. As a result,
current methods are hard to generalize to heterophilic graphs where dissimilar
nodes are widely connected, and also vulnerable to adversarial attacks. To
address this issue, we propose a novel unsupervised Graph Representation
learning method with Edge hEterophily discriminaTing (GREET) which learns
representations by discriminating and leveraging homophilic edges and
heterophilic edges. To distinguish two types of edges, we build an edge
discriminator that infers edge homophily/heterophily from feature and structure
information. We train the edge discriminator in an unsupervised way through
minimizing the crafted pivot-anchored ranking loss, with randomly sampled node
pairs acting as pivots. Node representations are learned through contrasting
the dual-channel encodings obtained from the discriminated homophilic and
heterophilic edges. With an effective interplaying scheme, edge discriminating
and representation learning can mutually boost each other during the training
phase. We conducted extensive experiments on 14 benchmark datasets and multiple
learning scenarios to demonstrate the superiority of GREET.

提出了一种新的无监督概率图表征学习方法 GREET，通过边鉴别器 (tedge discrimination) 学习鉴别同态和异态的边，采用对偶编码 (dual-channel encodings) 对同态和异态边进行对比，进一步强化边鉴别和表征学习之间的相互协作，提高了在异态图上的泛化能力和鲁棒性。