Speaker: Prof. Jia Wu, Macquarie University

Inviter: 周川副研究员

Title: Multi-view Graph Mining

Language: Chinese

Time & Venue: 2024.09.26 14：00-15：00 腾讯会议：449-954-975

Abstract: Graph classification has traditionally focused on graphs generated from a single feature view. In many applications, it is common to have useful information from different channels/views to describe objects, which naturally results in a new representation with multiple graphs generated from different feature views being used to describe one object. Here, we formulate a new Multi-Graph-View learning task for graph classification, where each object to be classified contains graphs from multiple graph-views. This problem setting is essentially different from traditional single-graph-view graph classification, where graphs are from one single feature view. To solve the problem, we propose a Cross Graph-View Sub graph Feature based Learning algorithm that explores an optimal set of sub graphs, across multiple graph-views, as features to represent graphs. Experiments and comparisons on real-world tasks demonstrate the algorithm’s performance.