HiGPT: Heterogeneous Graph Language Model

arXiv:2402.16024v1 Announce Type: cross
Abstract: Heterogeneous graph learning aims to capture complex relationships and diverse relational semantics among entities in a heterogeneous graph to obtain meaningful representations for nodes and edges. Recent advancements in heterogeneous graph neural networks (HGNNs) have achieved state-of-the-art performance by considering relation heterogeneity and using specialized message functions and aggregation rules. However, existing frameworks for heterogeneous graph learning have limitations in generalizing across diverse heterogeneous graph datasets. Most of these frameworks follow the "pre-train" and "fine-tune" …

abstract aggregation art arxiv cs.cl cs.lg diverse functions graph graph learning graph neural networks language language model networks neural networks nodes performance relational relationships rules semantics state type