  Directed graphs have been widely used in Community Question Answering
services (CQAs) to model asymmetric relationships among different types of
nodes in CQA graphs, e.g., question, answer, user. Asymmetric transitivity is
an essential property of directed graphs, since it can play an important role
in downstream graph inference and analysis. Question difficulty and user
expertise follow the characteristic of asymmetric transitivity. Maintaining
such properties, while reducing the graph to a lower dimensional vector
embedding space, has been the focus of much recent research. In this paper, we
tackle the challenge of directed graph embedding with asymmetric transitivity
preservation and then leverage the proposed embedding method to solve a
fundamental task in CQAs: how to appropriately route and assign newly posted
questions to users with the suitable expertise and interest in CQAs. The
technique incorporates graph hierarchy and reachability information naturally
by relying on a non-linear transformation that operates on the core
reachability and implicit hierarchy within such graphs. Subsequently, the
methodology levers a factorization-based approach to generate two embedding
vectors for each node within the graph, to capture the asymmetric transitivity.
Extensive experiments show that our framework consistently and significantly
outperforms the state-of-the-art baselines on two diverse real-world tasks:
link prediction, and question difficulty estimation and expert finding in
online forums like Stack Exchange. Particularly, our framework can support
inductive embedding learning for newly posted questions (unseen nodes during
training), and therefore can properly route and assign these kinds of questions
to experts in CQAs.
