Proposes a versatile online biologically plausible algorithm for learning sparse shift-invariant representations usable for clustering, manifold tiling, or sparse coding depending on data structure.
Maximizing Modularity is hard
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abstract
Several algorithms have been proposed to compute partitions of networks into communities that score high on a graph clustering index called modularity. While publications on these algorithms typically contain experimental evaluations to emphasize the plausibility of results, none of these algorithms has been shown to actually compute optimal partitions. We here settle the unknown complexity status of modularity maximization by showing that the corresponding decision version is NP-complete in the strong sense. As a consequence, any efficient, i.e. polynomial-time, algorithm is only heuristic and yields suboptimal partitions on many instances.
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cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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Flexible Online Representation Learning Based on Similarity Matching
Proposes a versatile online biologically plausible algorithm for learning sparse shift-invariant representations usable for clustering, manifold tiling, or sparse coding depending on data structure.