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Spectral Graph Clustering

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

Symmetric Nonnegative Matrix Factorization for Graph Clustering

Da Kuang, Haesun Park, C. Ding•Sat Dec 31 2011

Symmetric NMF is proposed as a general framework for graph clustering, which inherits the advantages of NMF by enforcing nonnegativity on the clustering assignment matrix, and serves as a potential basis for many extensions.

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AMOS: An automated model order selection algorithm for spectral graph clustering

Pin-Yu Chen, A. Hero, Thibaut Gensollen•Tue Sep 20 2016

This paper proposes AMOS, an automated model order selection algorithm for SGC that works by incrementally increasing the number of clusters, estimating the quality of identified clusters, and providing a series of clustering reliability tests.

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Phase Transitions and a Model Order Selection Criterion for Spectral Graph Clustering

Pin-Yu Chen, A. Hero•Sun Apr 10 2016

An automated model order selection (AMOS) is proposed, a solution to the SGC model selection problem under a random interconnection model using a novel selection criterion that is based on an asymptotic phase transition analysis.

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CLEAR: A Consistent Lifting, Embedding, and Alignment Rectification Algorithm for Multi-Agent Data Association

Yulun Tian, Kaveh Fathian, Kasra Khosoussi, Parker C. Lusk, Jonathan P. How•Mon Dec 31 2018

The CLEAR algorithm is presented, which fuses the techniques in the multi-way matching and the spectral clustering literature and provides consistent solutions, even in challenging high-noise regimes.

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An Internal Validity Index Based on Density-Involved Distance

Lianyu Hu, Caiming Zhong•Thu Mar 21 2019

A new robust distance measure, one into which density is incorporated, is designed to solve the problem, and an internal validity index based on this separation measure is then proposed, which can cope with both the spherical and non-spherical structure of clusters.

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Simultaneous Dimensionality and Complexity Model Selection for Spectral Graph Clustering

C. Priebe, Youngser Park, Congyuan Yang, D. Marchette•Thu Apr 04 2019

A novel model selection framework specifically for vertex clustering on graphs under a stochastic block model is established, and a theorem on the consistency of the estimates of model parameters is presented, providing support for the utility of the method.

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Ensemble clustering based on evidence extracted from the co-association matrix

Lianyu Hu, Caiming Zhong, Haiyong Xu, Xiaodong Yue, Ting Luo, Qiang Fu•Wed Jul 31 2019

This paper removes multiple level evidences having low occurrence frequencies from the co-association matrix, and uses normalized cut to achieve multiple clustering results, demonstrating that the proposed scheme outperforms some state-of-the-art clustering ensemble approaches.

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Refining a k-nearest neighbor graph for a computationally efficient spectral clustering

Mashaan Alshammari, J. Stavrakakis, M. Takatsuka•Mon May 31 2021

A refined version of $k$-nearest neighbor graph is proposed, in which data points are kept and number of edges are aggressively reduced for computational efficiency, which delivered a consistent performance despite significant reduction of edges.

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Latent structure blockmodels for Bayesian spectral graph clustering

Francesco Sanna Passino, N. Heard•Sat Jul 03 2021

A class of models called latent structure block models (LSBM) is proposed, allowing for graph clustering when community-specific one-dimensional manifold structure is present, and is shown to have a good performance on simulated and real-world network data.

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Learning Co-segmentation by Segment Swapping for Retrieval and Discovery

Alexei A. Efros, Armand Joulin, Mathieu Aubry, XI Shen•Thu Oct 28 2021

The goal of this work is to efficiently identify visually similar patterns in images, e.g. identifying an artwork detail copied between an engraving and an oil painting, or recognizing parts a night-time photograph visible in its daytime counterpart.

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