ADVANCING GRAPH NEURAL NETWORKS WITH HL-HGAT: A HODGE-LAPLACIAN AND ATTENTION MECHANISM APPROACH FOR HETEROGENEOUS GRAPH-STRUCTURED DATA

Abstract

Graph neural networks (GNNs) have proven effective in capturing relationships among nodes in a graph. This study introduces a novel perspective by considering a graph as a simplicial complex, encompassing nodes, edges, triangles, and k-simplices, enabling the definition of graph-structured data on any k-simplices. Our contribution is the Hodge-Laplacian heterogeneous graph attention network (HL-HGAT), designed to learn heterogeneous signal representations across k-simplices. The HL-HGAT incorporates three key components: HL convolutional filters (HL-filters), simplicial projection (SP), and simplicial attention pooling (SAP) operators, applied to k-simplices. HL-filters leverage the unique topology of k-simplices encoded by the Hodge-Laplacian (HL) operator, operating within the spectral domain of the k-th HL operator. To address computation challenges, we introduce a polynomial approximation for HL-filters, exhibiting spatial localization properties. Additionally, we propose a pooling operator to coarsen k-simplices, combining features through simplicial attention mechanisms of self-attention and cross-attention via transformers and SP operators, capturing topological interconnections across mul tiple dimensions of simplices. The HL-HGAT is comprehensively evaluated across diverse graph applications, including NP-hard problems, graph multi-label and classification challenges, and graph regression tasks in logistics, computer vision, biology, chemistry, and neuroscience. The results demonstrate the model’s efficacy and versatility in handling a wide range of graph-based scenarios.