  Graph Neural Networks (GNNs), originally proposed for node classification,
have also motivated many recent works on edge prediction (a.k.a., link
prediction). However, existing methods lack elaborate design regarding the
distinctions between two tasks that have been frequently overlooked: (i) edges
only constitute the topology in the node classification task but can be used as
both the topology and the supervisions (i.e., labels) in the edge prediction
task; (ii) the node classification makes prediction over each individual node,
while the edge prediction is determinated by each pair of nodes. To this end,
we propose a novel edge prediction paradigm named Edge-aware Message PassIng
neuRal nEtworks (EMPIRE). Concretely, we first introduce an edge splitting
technique to specify use of each edge where each edge is solely used as either
the topology or the supervision (named as topology edge or supervision edge).
We then develop a new message passing mechanism that generates the messages to
source nodes (through topology edges) being aware of target nodes (through
supervision edges). In order to emphasize the differences between pairs
connected by supervision edges and pairs unconnected, we further weight the
messages to highlight the relative ones that can reflect the differences. In
addition, we design a novel negative node-pair sampling trick that efficiently
samples 'hard' negative instances in the supervision instances, and can
significantly improve the performance. Experimental results verify that the
proposed method can significantly outperform existing state-of-the-art models
regarding the edge prediction task on multiple homogeneous and heterogeneous
graph datasets.
