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Given one or more query vertices, Community Search (CS) aims to find denselyintra-connected and loosely inter-connected structures containing queryvertices. Attributed Community Search (ACS), a related problem, is morechallenging since it finds communities with both cohesive structures andhomogeneous vertex attributes. However, most methods for the CS task rely oninflexible pre-defined structures and studies for ACS treat each attributeindependently. Moreover, the most popular ACS strategies decompose ACS into twoseparate sub-problems, i.e., the CS task and subsequent attribute filteringtask. However, in real-world graphs, the community structure and the vertexattributes are closely correlated to each other. This correlation is vital forthe ACS problem. In this paper, we propose Graph Neural Network models for bothCS and ACS problems, i.e., Query Driven-GNN and Attributed Query Driven-GNN. InQD-GNN, we combine the local query-dependent structure and global graphembedding. In order to extend QD-GNN to handle attributes, we model vertexattributes as a bipartite graph and capture the relation between attributes byconstructing GNNs on this bipartite graph. With a Feature Fusion operator,AQD-GNN processes the structure and attribute simultaneously and predictscommunities according to each attributed query. Experiments on real-worldgraphs with ground-truth communities demonstrate that the proposed modelsoutperform existing CS and ACS algorithms in terms of both efficiency andeffectiveness. More recently, an interactive setting for CS is proposed thatallows users to adjust the predicted communities. We further verify ourapproaches under the interactive setting and extend to the attributed context.Our method achieves 2.37% and 6.29% improvements in F1-score than thestate-of-the-art model without attributes and with attributes respectively.

Query Driven-Graph Neural Networks for Community Search: From Non-Attributed, Attributed, to Interactive Attributed