Spectral Graph Clustering

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Most implemented papers archive 2025-07-28

15 shown of 15 papers with code (30 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Random projection tree similarity metric for SpectralNet
  25 Feb 2023 1 repository listed
  Our experiments revealed that SpectralNet produces better clustering accuracy using rpTree similarity metric compared to k-nn graph with a distance metric.
• A parameter-free graph reduction for spectral clustering and SpectralNet
  25 Feb 2023 1 repository listed
  We introduce a graph reduction method that does not require any parameters.
• Approximate spectral clustering density-based similarity for noisy datasets
  22 Feb 2023 1 repository listed
  Also, CONN could be tricked by noisy density between clusters.
• Approximate spectral clustering with eigenvector selection and self-tuned k
  22 Feb 2023 1 repository listed
  The recently emerged spectral clustering surpasses conventional clustering methods by detecting clusters of any shape without the convexity assumption.
• Refining a k-nearest neighbor graph for a computationally efficient spectral clustering
  22 Feb 2023 1 repository listed
  We proposed a refined version of k-nearest neighbor graph, in which we keep data points and aggressively reduce number of edges for computational efficiency.
• Learning Co-segmentation by Segment Swapping for Retrieval and Discovery
  29 Oct 2021 1 repository listed
  The goal of this work is to efficiently identify visually similar patterns in images, e.
• Latent structure blockmodels for Bayesian spectral graph clustering
  4 Jul 2021 1 repository listed
  Furthermore, the presence of communities within the network might generate community-specific submanifold structures in the embedding, but this is not explicitly accounted for in most statistical models for networks.
• Refining a -nearest neighbor graph for a computationally efficient spectral clustering
  6 Feb 2021 1 repository listed
  We proposed a refined version of -nearest neighbor graph, in which we keep data points and aggressively reduce number of edges for computational efficiency.
• Ensemble clustering based on evidence extracted from the co-association matrix
  1 Aug 2019 1 repository listed
  The evidence accumulation model is an approach for collecting the information of base partitions in a clustering ensemble method, and can be viewed as a kernel transformation from the original data space to a…
• Simultaneous Dimensionality and Complexity Model Selection for Spectral Graph Clustering
  5 Apr 2019 1 repository listed
  The second contribution is a simultaneous model selection framework.
• An Internal Validity Index Based on Density-Involved Distance
  22 Mar 2019 1 repository listed
  One reason is that the measure of cluster separation does not consider the impact of outliers and neighborhood clusters.
• CLEAR: A Consistent Lifting, Embedding, and Alignment Rectification Algorithm for Multi-View Data Association
  6 Feb 2019 1 repository listed
  Many robotics applications require alignment and fusion of observations obtained at multiple views to form a global model of the environment.
• AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering
  21 Sep 2016 1 repository listed
  One of the longstanding problems in spectral graph clustering (SGC) is the so-called model order selection problem: automated selection of the correct number of clusters.
• Phase Transitions and a Model Order Selection Criterion for Spectral Graph Clustering
  11 Apr 2016 1 repository listed
  One of the longstanding open problems in spectral graph clustering (SGC) is the so-called model order selection problem: automated selection of the correct number of clusters.
• Symmetric Nonnegative Matrix Factorization for Graph Clustering
  27 Feb 2012 1 repository listed
  Unlike NMF, however, SymNMF is based on a similarity measure between data points, and factorizes a symmetric matrix containing pairwise similarity values (not necessarily nonnegative).