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SCGC : Self-supervised contrastive graph clustering
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DOI：10.1016/j.neucom.2024.128629.png)
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Abstract
En 中文
Graph clustering discovers groups or communities within networks. Increasingly, models use autoencoders to achieve effective clustering combined with Graph Neural Networks (GNN) for structure incorporation. However, GNNs based on convolution or attention variants lack dynamic fusion, suffer from over-smoothing, noise, node heterophily, are computationally expensive and typically require the complete graph being present. Instead, we propose SCGC, capable of dynamic soft structure fusion via augmentation-less edge- contrastive loss. Further, we propose SCGC*, with a more expressive novel distance metric, Influence, and our Influence Augmented Contrastive (IAC) loss, requiring only half the model parameters. Our models, SCGC and SCGC*, dynamically fuse discriminative node representations, jointly refine soft cluster assignments, completely eliminate convolutions and attention of traditional GNNs, use only simple linear units, and yet efficiently incorporate structure. They are impervious to layer depth; robust to over-smoothing, incorrect edges and heterophily; scalable by batching; augmentation-less; relaxes the homophily assumption and trivially parallelizable. We improve significantly over the state-of-the-art on a wide range of benchmarks, including images, sensor data, text, and citation networks, with superb efficiency. Specifically, 20% on ARI and 18% on NMI for DBLP; overall 55% reduction in training time and overall, 81% reduction on inference time. code: https://github.com/gayanku/SCGC.
Keywords:
Clustering
Self-supervised
Contrastive learning
MLP
Efficiency
Augmentation-less
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