  Community detection has long been an important yet challenging task to
analyze complex networks with a focus on detecting topological structures of
graph data. Essentially, real-world graph data contains various features, node
and edge types which dynamically vary over time, and this invalidates most
existing community detection approaches. To cope with these issues, this paper
proposes the heterogeneous-temporal graph convolutional networks (HTGCN) to
detect communities from hetergeneous and temporal graphs. Particularly, we
first design a heterogeneous GCN component to acquire feature representations
for each heterogeneous graph at each time step. Then, a residual compressed
aggregation component is proposed to represent "dynamic" features for "varying"
communities, which are then aggregated with "static" features extracted from
current graph. Extensive experiments are evaluated on two real-world datasets,
i.e., DBLP and IMDB. The promising results demonstrate that the proposed HTGCN
is superior to both benchmark and the state-of-the-art approaches, e.g., GCN,
GAT, GNN, LGNN, HAN and STAR, with respect to a number of evaluation criteria.
