异构超图神经网络的自监督预训练
Self-Supervised Pretraining for Heterogeneous Hypergraph Neural Networks
AI总结：
提出用于异构超图神经网络的自监督预训练框架SPHH，通过两个预训练任务学习局部和全局表示，在多种下游任务中显著提升性能并展现鲁棒性。
摘要：
最近，图神经网络（GNNs）的预训练方法在从无标签图数据中学习有效表示方面取得了成功。然而，这些方法大多依赖于图中的成对关系，未能捕捉实体间潜在的高阶关系。超图是一种通用且富有表现力的结构，能够有效建模数据中实体间的高阶关系。尽管已有将GNNs适配到超图的努力（HyperGNN），但目前尚无针对异构超图上HyperGNN的完全自监督预训练方法。在本文中，我们提出了SPHH，一种用于异构HyperGNN的新型自监督预训练框架。我们的方法能够以自监督方式有效捕捉数据中实体间的高阶关系。SPHH由两个自监督预训练任务组成，旨在通过利用源自超图结构的信息性表示，同时学习超图中实体的局部和全局表示。总体而言，我们的工作在HyperGNNs的自监督预训练领域取得了重大进展，并有望提升各种基于图的下游任务的性能，如节点分类和链路预测任务，这些任务被映射到超图配置中。我们在两个真实世界基准上使用四种不同的HyperGNN模型进行的实验表明，我们提出的SPHH框架在各种下游任务中始终优于最先进的基线。结果证明，SPHH能够提升各种HyperGNN模型在各种下游任务中的性能，无论其架构或复杂性如何，这凸显了我们框架的鲁棒性。
英文摘要：
Recently, pretraining methods for the Graph Neural Networks (GNNs) have been successful at learning effective representations from unlabeled graph data. However, most of these methods rely on pairwise relations in the graph and do not capture the underling higher-order relations between entities. Hypergraphs are versatile and expressive structures that can effectively model higher-order relationships among entities in the data. Despite the efforts to adapt GNNs to hypergraphs (HyperGNN), there are currently no fully self-supervised pretraining methods for HyperGNN on heterogeneous hypergraphs. In this paper, we present SPHH, a novel self-supervised pretraining framework for heterogeneous HyperGNNs. Our method is able to effectively capture higher-order relations among entities in the data in a self-supervised manner. SPHH is consist of two self-supervised pretraining tasks that aim to simultaneously learn both local and global representations of the entities in the hypergraph by using informative representations derived from the hypergraph structure. Overall, our work presents a significant advancement in the field of self-supervised pretraining of HyperGNNs, and has the potential to improve the performance of various graph-based downstream tasks such as node classification and link prediction tasks which are mapped to hypergraph configuration. Our experiments on two real-world benchmarks using four different HyperGNN models show that our proposed SPHH framework consistently outperforms state-of-the-art baselines in various downstream tasks. The results demonstrate that SPHH is able to improve the performance of various HyperGNN models in various downstream tasks, regardless of their architecture or complexity, which highlights the robustness of our framework.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
