  Searching for local communities is an important research challenge that
allows for personalized community discovery and supports advanced data analysis
in various complex networks, such as the World Wide Web, social networks, and
brain networks. The evolution of these networks over time has motivated several
recent studies to identify local communities in temporal networks. Given any
query nodes, Community Search aims to find a densely connected subgraph
containing query nodes. However, existing community search approaches in
temporal networks have two main limitations: (1) they adopt pre-defined
subgraph patterns to model communities, which cannot find communities that do
not conform to these patterns in real-world networks, and (2) they only use the
aggregation of disjoint structural information to measure quality, missing the
dynamic of connections and temporal properties. In this paper, we propose a
query-driven Temporal Graph Convolutional Network (CS-TGN) that can capture
flexible community structures by learning from the ground-truth communities in
a data-driven manner. CS-TGN first combines the local query-dependent structure
and the global graph embedding in each snapshot of the network and then uses a
GRU cell with contextual attention to learn the dynamics of interactions and
update node embeddings over time. We demonstrate how this model can be used for
interactive community search in an online setting, allowing users to evaluate
the found communities and provide feedback. Experiments on real-world temporal
graphs with ground-truth communities validate the superior quality of the
solutions obtained and the efficiency of our model in both temporal and
interactive static settings.
