CS-MLGCN : Multiplex Graph Convolutional Networks for Community Search in Multiplex Networks
This work addresses the challenge of finding personalized communities in multiplex networks for applications like biological and social systems, representing an incremental improvement by introducing a data-driven approach over pattern-based methods.
The paper tackles the problem of community search in multiplex networks, where existing methods rely on predefined subgraph patterns that may not capture real-world communities, and proposes CS-MLGCN, a query-driven graph convolutional network that learns flexible community structures from ground-truth data, achieving validated quality and efficiency in experiments on real-world graphs.
Community Search (CS) is one of the fundamental tasks in network science and has attracted much attention due to its ability to discover personalized communities with a wide range of applications. Given any query nodes, CS seeks to find a densely connected subgraph containing query nodes. Most existing approaches usually study networks with a single type of proximity between nodes, which defines a single view of a network. However, in many applications such as biological, social, and transportation networks, interactions between objects span multiple aspects, yielding networks with multiple views, called multiplex networks. Existing CS approaches in multiplex networks adopt pre-defined subgraph patterns to model the communities, which cannot find communities that do not have such pre-defined patterns in real-world networks. In this paper, we propose a query-driven graph convolutional network in multiplex networks, CS-MLGCN, that can capture flexible community structures by learning from the ground-truth communities in a data-driven fashion. CS-MLGCN first combines the local query-dependent structure and global graph embedding in each type of proximity and then uses an attention mechanism to incorporate information on different types of relations. Experiments on real-world graphs with ground-truth communities validate the quality of the solutions we obtain and the efficiency of our model.