  Many real-world tasks solved by heterogeneous network embedding methods can
be cast as modeling the likelihood of pairwise relationship between two nodes.
For example, the goal of author identification task is to model the likelihood
of a paper being written by an author (paper-author pairwise relationship).
Existing task-guided embedding methods are node-centric in that they simply
measure the similarity between the node embeddings to compute the likelihood of
a pairwise relationship between two nodes. However, we claim that for
task-guided embeddings, it is crucial to focus on directly modeling the
pairwise relationship. In this paper, we propose a novel task-guided pair
embedding framework in heterogeneous network, called TaPEm, that directly
models the relationship between a pair of nodes that are related to a specific
task (e.g., paper-author relationship in author identification). To this end,
we 1) propose to learn a pair embedding under the guidance of its associated
context path, i.e., a sequence of nodes between the pair, and 2) devise the
pair validity classifier to distinguish whether the pair is valid with respect
to the specific task at hand. By introducing pair embeddings that capture the
semantics behind the pairwise relationships, we are able to learn the
fine-grained pairwise relationship between two nodes, which is paramount for
task-guided embedding methods. Extensive experiments on author identification
task demonstrate that TaPEm outperforms the state-of-the-art methods,
especially for authors with few publication records.
