Maximum Clique Approximation

Maximum Clique Approximation is an approximation algorithm designed to find a maximal complete subgraph in an undirected graph, differing from exact clique algorithms by providing a computationally efficient solution with a guaranteed performance ratio relative to the optimal clique size.

108

Publications

9.4K

Citations

241

Authors

148

Institutions

Publications per year

2017–2026

18

Authors

241

  1. AJ
    Arun Jagota

    University of California, Santa Cruz, United States

    4 publications · 133 citations

    4

    h-index

  2. MS
    Márió Szegedy

    Rutgers, The State University of New Jersey, United States

    3 publications · 1.3K citations

    3

    h-index

  3. BB
    Binay Bhattacharya

    Simon Fraser University, Canada

    3 publications · 49 citations

    3

    h-index

  4. EB
    Egon Balas

    Carnegie Mellon University, United States

    3 publications · 393 citations

    3

    h-index

  5. SB
    Sergiy Butenko

    Texas A&M University, United States

    3 publications · 82 citations

    3

    h-index

1–5 of 241

Venues

  1. Information Processing Letters

    5 publications · 137 citations

    5

    h-index

  2. Algorithmica

    4 publications · 147 citations

    4

    h-index

  3. Discrete Applied Mathematics

    4 publications · 696 citations

    4

    h-index

  4. SIAM Journal on Computing

    4 publications · 449 citations

    4

    h-index

  5. Theoretical Computer Science

    4 publications · 182 citations

    4

    h-index

1–5