Massive Quasi-Clique Detection

By James Abello, Maurício Resende, Sandra Sudarsky. Latin American Symposium on Theoretical Informatics, 2002

377 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/72b2e9bd1d780106797767e82133a69e1a8e8a78

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Citations in the corpus

Greedy Randomized Adaptive Search Procedures. (2003, neutral): “• routing [25, 29, 34, 45, 47, 53, 101, 108] • logic [58, 75, 133, 147, 151, 152] • covering and partitioning [10, 23, 26, 68, 85, 92] • location [50, 54, 93, 1, 96, 57, 99, 130, 174, 175] • minimum Steiner tree [46, 119, 120, 121, 163] • optimization in graphs [2, 3, 4, 28, 70, 76, 77, 97, 102, 105”
Clique Relaxations in Social Network Analysis: The Maximum k-Plex Problem (2011, methodology): “As suggested by Seidman, this approach was only to produce global measures that captured the cohesive subgroups as well as regions surrounding them.”
A review on algorithms for maximum clique problems (2015, neutral): “GRASP procedures are studied in Abello, Resende, & Sudarsky (2002) for detecting large quasi-cliques in graphs representing telecommunications data.”
Community Detection and Mining in Social Media (2010, methodology): “A subgraph Gs(Vs, Es) is γ -dense (also called a quasi-clique (Abello et al., 2002)) if”
A survey of frequent subgraph mining algorithms (2012, background): “Example applications where the mining of cliques, or quasi-cliques, has been applied includes: community mining (Abello et al. 2002), gene expression mining (Pei et al. 2005), and the discovery of highly correlated stocks from stock market graphs (Wang et al. 2006).”
GRASP: Greedy Randomized Adaptive Search Procedures (2003, methodology): “GRASP has been applied to many problems in different areas, such as routing [18, 21, 36, 75, 105]; logic [40, 54, 98, 109, 113, 114]; covering and partitioning [12, 17, 48, 65]; location [1, 33, 37, 66, 69, 39, 73, 96, 139, 140]; minimum Steiner tree [32, 84, 85, 88, 128]; optimization in graphs [2,”
Discovering Large Dense Subgraphs in Massive Graphs (2005, methodology): “Abello et al. [1] consider dense subgraph extraction based on a notion of pruning candidate sets of nodes, in a model in which the nodes but not the edges of the graph may be memory-resident.”
Denser than the densest subgraph: extracting optimal quasi-cliques with quality guarantees (2013, methodology): “[1] propose an algorithm for finding a single maximal α-quasi-clique, while Uno [31] introduces an algorithm to enumerate all α-quasi-cliques.”
Truss Decomposition in Massive Networks (2012, background): “The quasi-clique can be either a relaxation on the density [1] or the degree [23, 26].”
FENNEL: streaming graph partitioning for massive scale graphs (2014, background): “This is intimately related to the concepts of graph modularity [21, 47, 46] and quasi-cliques [4].”