AGHINT: Attribute-Guided Representation Learning on Heterogeneous Information Networks with Transformer

arXiv:2404.10443v1 Announce Type: new
Abstract: Recently, heterogeneous graph neural networks (HGNNs) have achieved impressive success in representation learning by capturing long-range dependencies and heterogeneity at the node level. However, few existing studies have delved into the utilization of node attributes in heterogeneous information networks (HINs). In this paper, we investigate the impact of inter-node attribute disparities on HGNNs performance within the benchmark task, i.e., node classification, and empirically find that typical models exhibit significant performance decline when classifying nodes whose …

abstract arxiv cs.ai cs.lg dependencies graph graph neural networks however information networks neural networks node paper representation representation learning studies success transformer type