  Given one or more query vertices, Community Search (CS) aims to find densely
intra-connected and loosely inter-connected structures containing query
vertices. Attributed Community Search (ACS), a related problem, is more
challenging since it finds communities with both cohesive structures and
homogeneous vertex attributes. However, most methods for the CS task rely on
inflexible pre-defined structures and studies for ACS treat each attribute
independently. Moreover, the most popular ACS strategies decompose ACS into two
separate sub-problems, i.e., the CS task and subsequent attribute filtering
task. However, in real-world graphs, the community structure and the vertex
attributes are closely correlated to each other. This correlation is vital for
the ACS problem. In this paper, we propose Graph Neural Network models for both
CS and ACS problems, i.e., Query Driven-GNN and Attributed Query Driven-GNN. In
QD-GNN, we combine the local query-dependent structure and global graph
embedding. In order to extend QD-GNN to handle attributes, we model vertex
attributes as a bipartite graph and capture the relation between attributes by
constructing GNNs on this bipartite graph. With a Feature Fusion operator,
AQD-GNN processes the structure and attribute simultaneously and predicts
communities according to each attributed query. Experiments on real-world
graphs with ground-truth communities demonstrate that the proposed models
outperform existing CS and ACS algorithms in terms of both efficiency and
effectiveness. More recently, an interactive setting for CS is proposed that
allows users to adjust the predicted communities. We further verify our
approaches under the interactive setting and extend to the attributed context.
Our method achieves 2.37% and 6.29% improvements in F1-score than the
state-of-the-art model without attributes and with attributes respectively.
