  Cyberspace hosts abundant interactions between users and different kinds of
objects, and their relations are often encapsulated as bipartite graphs.
Detecting user community in such heterogeneous graphs is an essential task to
uncover user information needs and to further enhance recommendation
performance. While several main cyber domains carrying high-quality graphs,
unfortunately, most others can be quite sparse. However, as users may appear in
multiple domains (graphs), their high-quality activities in the main domains
can supply community detection in the sparse ones, e.g., user behaviors on
Google can help thousands of applications to locate his/her local community
when s/he uses Google ID to login those applications. In this paper, our model,
Pairwise Cross-graph Community Detection (PCCD), is proposed to cope with the
sparse graph problem by involving external graph knowledge to learn user
pairwise community closeness instead of detecting direct communities.
Particularly in our model, to avoid taking excessive propagated information, a
two-level filtering module is utilized to select the most informative
connections through both community and node level filters. Subsequently, a
Community Recurrent Unit (CRU) is designed to estimate pairwise user community
closeness. Extensive experiments on two real-world graph datasets validate our
model against several strong alternatives. Supplementary experiments also
validate its robustness on graphs with varied sparsity scales.
