  This paper describes an end-to-end solution for the relationship prediction
task in heterogeneous, multi-relational graphs. We particularly address two
building blocks in the pipeline, namely heterogeneous graph representation
learning and negative sampling. Existing message passing-based graph neural
networks use edges either for graph traversal and/or selection of message
encoding functions. Ignoring the edge semantics could have severe repercussions
on the quality of embeddings, especially when dealing with two nodes having
multiple relations. Furthermore, the expressivity of the learned representation
depends on the quality of negative samples used during training. Although
existing hard negative sampling techniques can identify challenging negative
relationships for optimization, new techniques are required to control false
negatives during training as false negatives could corrupt the learning
process. To address these issues, first, we propose RelGNN -- a message
passing-based heterogeneous graph attention model. In particular, RelGNN
generates the states of different relations and leverages them along with the
node states to weigh the messages. RelGNN also adopts a self-attention
mechanism to balance the importance of attribute features and topological
features for generating the final entity embeddings. Second, we introduce a
parameter-free negative sampling technique -- adaptive self-adversarial (ASA)
negative sampling. ASA reduces the false-negative rate by leveraging positive
relationships to effectively guide the identification of true negative samples.
Our experimental evaluation demonstrates that RelGNN optimized by ASA for
relationship prediction improves state-of-the-art performance across
established benchmarks as well as on a real industrial dataset.
