  Community Search (CS) is one of the fundamental tasks in network science and
has attracted much attention due to its ability to discover personalized
communities with a wide range of applications. Given any query nodes, CS seeks
to find a densely connected subgraph containing query nodes. Most existing
approaches usually study networks with a single type of proximity between
nodes, which defines a single view of a network. However, in many applications
such as biological, social, and transportation networks, interactions between
objects span multiple aspects, yielding networks with multiple views, called
multiplex networks. Existing CS approaches in multiplex networks adopt
pre-defined subgraph patterns to model the communities, which cannot find
communities that do not have such pre-defined patterns in real-world networks.
In this paper, we propose a query-driven graph convolutional network in
multiplex networks, CS-MLGCN, that can capture flexible community structures by
learning from the ground-truth communities in a data-driven fashion. CS-MLGCN
first combines the local query-dependent structure and global graph embedding
in each type of proximity and then uses an attention mechanism to incorporate
information on different types of relations. Experiments on real-world graphs
with ground-truth communities validate the quality of the solutions we obtain
and the efficiency of our model.
