L$^2$GC: Lorentzian Linear Graph Convolutional Networks For Node Classification

• DOI: 10.48550/arxiv.2403.06064
• Publication Date: 2024-03-09
• Authors: Qiuyu Liang; Weihua Wang; Feilong Bao; Guanglai Gao
• Keywords: FOS: Computer and information sciences; Computer Science - Machine Learning; Computer Science - Computation and Language; Artificial Intelligence (cs.AI); Computer Science - Artificial Intelligence; Computation and Language (cs.CL); Machine Learning (cs.LG)

Linear Graph Convolutional Networks (GCNs) are used to classify the node in graph data. However, we note that most existing linear GCN models perform neural network operations Euclidean space, which do not explicitly capture tree-like hierarchical structure exhibited real-world datasets modeled as graphs. In this paper, attempt introduce hyperbolic space into and propose a novel framework for Lorentzian GCN. Specifically, map learned features of nodes then feature transformation underlying Experimental results on standard citation networks with semi-supervised learning show our approach yields new state-of-the-art accuracy 74.7$\%$ Citeseer 81.3$\%$ PubMed datasets. Furthermore, observe can be trained up two orders magnitude faster than other nonlinear dataset. Our code is publicly available at https://github.com/llqy123/LLGC-master.