VICS-GNN: A Visual Interactive System for Community Search via Graph Neural Network

Abstract

Community Search, which locates the desired sub-graph containing the query node, is a fundamental operation in network analysis. Most of the existing systems rely on pre-defined rules to find the community, while we argue that the target community is always specific for different purposes and the pre-defined rules may not be suitable. In this work, we demonstrate VICS-GNN, a Visual Interactive system for Community Search via graph Neural Network. VICS-GNN provides end users with a flexible, user-friendly front end to manage and explore the sub-graph around the query node, allows users labeling nodes to guide G NN models in learning community rules by combining content and structural features, and locates the community interactively and iteratively. In the demonstration, demo visitors will be invited to experience the VICS-GNN system using real-world data from Wikipedia and Sina Weibo to feel how convenient and intuitive it is to help with community search.