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Research map / Graph learning / Graph algorithms and clustering

Provable Overlapping Community Detection in Weighted Graphs

NeurIPS 2020 · Graph learning › Graph algorithms and clustering · approach: Graph distance and retrieval · 4 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use SP+LP instead of ClusterONEProtein complex detection · Krogan core, SGDScore: 1.748 -> 1.512Hurt
Use SP+LP instead of ClusterONEProtein complex detection · Krogan extended, SGDScore: 1.586 -> 1.602Helped
Use SP+LP instead of ClusterONEProtein complex detection · Collins, SGDScore: 2.091 -> 1.433Hurt
Use SP+LP instead of ClusterONEProtein complex detection · Krogan core, MIPSScore: 1.424 -> 1.153Hurt
Use SP+LP instead of ClusterONEProtein complex detection · Krogan extended, MIPSScore: 1.277 -> 1.231Hurt
Use SP+LP instead of ClusterONEProtein complex detection · Collins, MIPSScore: 1.755 -> 1.219Hurt
Use SP+LP instead of GeoNMFSynthetic MMSB weighted graphs · Synthetic graphs as n increasesEntrywise errorHelped
Use SP+LP instead of GeoNMFSynthetic MMSB weighted graphs · Synthetic graphs as k increasesEntrywise errorHelped
Use SP+LP instead of GeoNMFSynthetic MMSB weighted graphs · Synthetic graphs as α increasesEntrywise errorHelped
Use SP+LP instead of GeoNMFSynthetic MMSB weighted graphs · Synthetic graphs as δ increasesEntrywise errorHelped
Use SP+LP instead of GeoNMFSynthetic MMSB weighted graphs · Synthetic graphsRunning timeHurt

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