  Representation learning methods for heterogeneous networks produce a
low-dimensional vector embedding for each node that is typically fixed for all
tasks involving the node. Many of the existing methods focus on obtaining a
static vector representation for a node in a way that is agnostic to the
downstream application where it is being used. In practice, however, downstream
tasks such as link prediction require specific contextual information that can
be extracted from the subgraphs related to the nodes provided as input to the
task. To tackle this challenge, we develop SLiCE, a framework bridging static
representation learning methods using global information from the entire graph
with localized attention driven mechanisms to learn contextual node
representations. We first pre-train our model in a self-supervised manner by
introducing higher-order semantic associations and masking nodes, and then
fine-tune our model for a specific link prediction task. Instead of training
node representations by aggregating information from all semantic neighbors
connected via metapaths, we automatically learn the composition of different
metapaths that characterize the context for a specific task without the need
for any pre-defined metapaths. SLiCE significantly outperforms both static and
contextual embedding learning methods on several publicly available benchmark
network datasets. We also interpret the semantic association matrix and provide
its utility and relevance in making successful link predictions between
heterogeneous nodes in the network.
