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School of Computing, Macquarie University Dr Shan Xue Self-supervised Heterogeneous Hypergraph Learning with Context-aware Pooling for Graph-level Classification
发布人: | 发布日期:2023年11月14日 17:42 | 点击数:

讲座时间:202311月241300

讲座地点:B105

讲座对象:bwin必赢唯一官方网站师生

讲座摘要:

Representation learning in heterogeneous graphs is challenging due to their complexity. Current self-supervised learning (SSL) methods mainly focus on node-level tasks, neglecting global graph features. They also use computationally demanding techniques and overlook certain node relationships, reducing graph-level learning effectiveness. To address these issues, we introduce a novel SSL framework for heterogeneous hypergraph learning. This framework uses a k-hop neighborhood method and a shared attribute system instead of traditional meta-path techniques. We also offer a context-aware pooling system and a high-order-aware augmentation strategy, improving graph-level learning. Tested against various baselines, our model shows a 5.81% performance boost.