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Research map / Graph learning / Graph algorithms and clustering

p-Norm Flow Diffusion for Local Graph Clustering

ICML 2020 · Graph learning › Graph algorithms and clustering · approach: Neural and local graph clustering · 3 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Raise p-norm flow diffusion from p=2 to p=4 (ablation)p-norm flow diffusion · LFR synthetic graphsconductanceHelped
Raise p-norm flow diffusion from p=4 to p=8 (ablation)p-norm flow diffusion · LFR synthetic graphsconductanceHelped
Increase the seed-set overlap with the target clusterp-norm flow diffusion · LFR synthetic graph, μ = 0.3conductanceHelped
Increase the seed-set overlap with the target clusterp-norm flow diffusion · LFR synthetic graph, μ = 0.3F1 measureHelped
Use p=2 instead of p=4 or p=8 (ablation)p-norm flow diffusion · LFR synthetic graphsF1 measureHurt
Use p=4 instead of p=2 (ablation)p-norm flow diffusion · Dumbbell graphconductanceHelped
Use p=4 instead of p=2 (ablation)p-norm flow diffusion · Facebook real-world graphsconductanceHelped
Use p=4 instead of p=2 (ablation)p-norm flow diffusion · LFR synthetic graphsconductanceHelped
Use p=4 instead of p=2 (ablation)p-norm flow diffusion · LFR synthetic graphsF1 measureHelped
Use nonlinear power diffusion rather than p-norm flow diffusionlocal graph clustering · SfldF1 measureHurt

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