March 14, 2024, 4:41 a.m. | Junwei Su, Lingjun Mao, Chuan Wu

cs.LG updates on arXiv.org arxiv.org

arXiv:2403.08207v1 Announce Type: new
Abstract: Many computer vision and machine learning problems are modelled as learning tasks on heterogeneous graphs, featuring a wide array of relations from diverse types of nodes and edges. Heterogeneous graph neural networks (HGNNs) stand out as a promising neural model class designed for heterogeneous graphs. Built on traditional GNNs, existing HGNNs employ different parameter spaces to model the varied relationships. However, the practical effectiveness of existing HGNNs is often limited to simple heterogeneous graphs with …

abstract array arxiv class computer computer vision cs.lg diverse graph graph neural network graph neural networks graphs machine machine learning network networks neural network neural networks nodes relations scalable tasks type types vision

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