VICS-GNN: A Visual Interactive System for Community Search Via Graph Neural Network
2022 IEEE 38th International Conference on Data Engineering (ICDE)(2022)
摘要
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.
更多查看译文
关键词
Interactive Community Search,Graph Neural Network,Visualization,Social Network
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要