🤖 AI Summary
Learning scalable node representations in graphs without node features and with only partial pairwise labels remains challenging. This work proposes Contrastive FUSE, a novel framework that, for the first time, integrates modularity-inspired structural learning with contrastive supervision, directly optimizing a spectral contrastive objective using community-aware structural signals and signed pairwise constraints. To enhance computational efficiency, the method introduces a lightweight gradient approximation to replace the costly exact modularity gradients and incorporates optimization techniques such as natural gradient decomposition and adaptive learning rate scaling. Experimental results demonstrate that Contrastive FUSE achieves classification performance on multiple benchmark graphs that is either superior or comparable to existing approaches—all without requiring node features—while significantly accelerating training compared to baseline methods.
📝 Abstract
We introduce Contrastive FUSE, a fast and unified framework for scalable node representation learning in graphs with partially available pairwise node labels and no available node features. Unlike existing methods, we directly optimize a spectral contrastive objective that integrates community-aware structural signals with signed pairwise constraints. To support large-scale training, we replace the expensive modularity gradient with a lightweight approximation, which preserves the structure-seeking behavior of modularity while reducing the computational cost significantly. This yields an efficient optimization scheme with a natural gradient decomposition and adaptive learning-rate scaling, enabling fast iterative updates even on million-edge graphs. Extensive experiments on benchmark citation networks, large co-purchase graphs, and OGB datasets show that Contrastive FUSE achieves competitive or superior contrastive classification performance without relying on node features, while offering substantial runtime gains over existing baselines. These results highlight the effectiveness of coupling modularity-inspired structural learning with contrastive supervision for efficient and scalable contrastive node representation learning.