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Multi-view Contrastive Graph Clustering

NeurIPS 2021 · Graph learning › Graph self-supervised learning · approach: Augmentation-free graph learning · 2 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add contrastive regularizer J (vs Frobenius term) (ablation)MCGC · ACMACC: 0.8334 -> 0.9147Helped
Add contrastive regularizer J (vs Frobenius term) (ablation)MCGC · DBLPACC: 0.7658 -> 0.9298Helped
Add contrastive regularizer J (vs Frobenius term) (ablation)MCGC · IMDBACC: 0.5636 -> 0.6182Helped
Add contrastive regularizer J (vs Frobenius term) (ablation)MCGC · Amazon photosACC: 0.5882 -> 0.7164Helped
Add contrastive regularizer J (vs Frobenius term) (ablation)MCGC · Amazon computersACC: 0.4662 -> 0.5967Helped
Add contrastive regularizer J (vs Frobenius term) (ablation)MCGC · IMDBF1: 0.4444 -> 0.4401No clear effect
Use both graph views G1 and G2 instead of only G1 (ablation)MCGC · ACMACC: 0.9088 -> 0.9147Helped
Use both graph views instead of only G2 (ablation)MCGC · ACMACC: 0.8152 -> 0.9147Helped
Use both feature views instead of only X1 (ablation)MCGC · Amazon photosACC: 0.4433 -> 0.7164Helped
Use both feature views instead of only X2 (ablation)MCGC · Amazon photosACC: 0.6935 -> 0.7164Helped
Add graph filtering (vs MCGC-) (ablation)MCGC · ACMACC: 0.9061 -> 0.9147Helped
Add graph filtering (vs MCGC-) (ablation)MCGC · DBLPACC: 0.9162 -> 0.9298Helped

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