Spectral Graph Algorithms
Spectral Graph Algorithms is a class of graph algorithms that leverage the eigenvalues and eigenvectors of matrices associated with a graph—such as the adjacency or Laplacian matrix—to analyze and solve problems related to graph structure, partitioning, and embedding.
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2017–2026
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Spectral grouping using the nystrom method
Charless C. Fowlkes, Serge Belongie, Fan Chung et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence · 2004 · 1.3K citations · Full text
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University of California, Berkeley
Berkeley, United States
18 h-index
30 publications
9.7K citations
Carnegie Mellon University
Pittsburgh, United States
18 h-index
40 publications
5K citations
Massachusetts Institute of Technology
Cambridge, United States
13 h-index
37 publications
1.5K citations
Stanford University
Stanford, United States
12 h-index
22 publications
1.1K citations
Yale University
New Haven, United States
11 h-index
21 publications
1.9K citations

Northwestern Polytechnical University
Xi'an, China
10 h-index
25 publications
1.6K citations

Indian Institute of Science Bangalore
Bengaluru, India
10 h-index
27 publications
1.2K citations
Georgia Institute of Technology
Atlanta, United States
10 h-index
18 publications
749 citations
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53 publications · 1.7K citations
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Repository · 22 publications · 567 citations
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TPAMI · 7 publications · 1.9K citations
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