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Community Search (CS) is one of the fundamental tasks in network science andhas attracted much attention due to its ability to discover personalizedcommunities with a wide range of applications. Given any query nodes, CS seeksto find a densely connected subgraph containing query nodes. Most existingapproaches usually study networks with a single type of proximity betweennodes, which defines a single view of a network. However, in many applicationssuch as biological, social, and transportation networks, interactions betweenobjects span multiple aspects, yielding networks with multiple views, calledmultiplex networks. Existing CS approaches in multiplex networks adoptpre-defined subgraph patterns to model the communities, which cannot findcommunities that do not have such pre-defined patterns in real-world networks.In this paper, we propose a query-driven graph convolutional network inmultiplex networks, CS-MLGCN, that can capture flexible community structures bylearning from the ground-truth communities in a data-driven fashion. CS-MLGCNfirst combines the local query-dependent structure and global graph embeddingin each type of proximity and then uses an attention mechanism to incorporateinformation on different types of relations. Experiments on real-world graphswith ground-truth communities validate the quality of the solutions we obtainand the efficiency of our model.

CS-MLGCN : Multiplex Graph Convolutional Networks for Community Search in Multiplex Networks