  Recently, attributed community search, a related but different problem to
community detection and graph clustering, has been widely studied in the
literature. Compared with the community detection that finds all existing
static communities from a graph, the attributed community search (ACS) is more
challenging since it aims to find dynamic communities with both cohesive
structures and homogeneous node attributes given arbitrary queries. To solve
the ACS problem, the most popular paradigm is to simplify the problem as two
sub-problems, that is, structural matching and attribute filtering and deal
with them separately. However, in real-world graphs, the community structure
and the node attributes are actually correlated to each other. In this vein,
current studies cannot capture these correlations which are vital for the ACS
problem.
  In this paper, we propose Query-Driven Graph Convolutional Networks (QD-GCN),
an end-to-end framework that unifies the community structure as well as node
attribute to solve the ACS problem. In particular, QD-GCN leverages the Graph
Convolutional Networks, which is a powerful tool to encode the graph topology
and node attributes concurrently, as the backbones to extract the
query-dependent community information from the original graph. By utilizing
this query-dependent community information, QD-GCN is able to predict the
target community given any queries. Experiments on real-world graphs with
ground-truth communities demonstrate that QD-GCN outperforms existing
attributed community search algorithms in terms of both efficiency and
effectiveness.
