Beyond smoothing: Unsupervised graph representation learning with edge heterophily discriminating

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