Denser than the densest subgraph: extracting optimal quasi-cliques with quality guarantees
By Charalampos E. Tsourakakis, Francesco Bonchi, Aristides Gionis, Francesco Gullo, Maria A. Tsiarli. Knowledge Discovery and Data Mining, 2013
340 citations recorded by the source corpus.
Subfields: Computational Theory and Mathematics, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/6f325ac1385050e17110dbf1b0ff01fec22ca7b1
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In Computational Theory and Mathematics
In Computer Vision and Pattern Recognition
Citations in the corpus
- Querying k-truss community in large and dynamic graphs (2014, methodology): “There are different definitions of dense subgraph patterns, including clique [3, 5, 22, 24], quasi-clique [19], k -core [2, 4], k -truss [7, 21, 26], dense neighborhood graph [23], dense bipartite subgraph [12], etc. Clique and quasi-clique enumeration methods include the classical algorithm [3],…”
- Local Modeling of Attributed Graphs: Algorithms and Applications (2016, methodology): “average degree, edge density) and have been used for dense subgraph mining [28, 139] and anomaly detection [2].”
- A survey of community search over big graphs (2019, methodology): “[180] computed the quasi-clique-based dense subgraphs, and Qin et al.”