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

TL;DR

This paper introduces CS-MLGCN, a novel graph convolutional network that effectively identifies personalized communities in multiplex networks by learning flexible community structures through a data-driven, query-dependent approach.

Contribution

It proposes a query-driven multiplex graph convolutional network that captures flexible community structures without relying on pre-defined patterns, improving community search in multiplex networks.

Findings

Outperforms existing methods on real-world multiplex networks.

Effectively captures diverse community structures.

Demonstrates high efficiency and accuracy in community detection.

Abstract

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.