Mining Largest Maximal Quasi-Cliques

Quasi-cliques are dense incomplete subgraphs of a graph that generalize the notion cliques. Enumerating quasi-cliques from is robust way to detect densely connected structures with applications in bioinformatics and social network analysis. However, enumerating challenging problem, even harder than problem We consider enumeration top- k degree-based make following contributions: (1) we show detecting whether given quasi-clique maximal (i.e., not contained within another quasi-clique) NP-hard. (2) present novel heuristic algorithm K ernel QC enumerate largest graph. Our method based on identifying kernels extremely graph, followed by growing around these kernels, arrive at required densities. (3) Experimental results our accurately enumerates much faster current state-of-the-art methods for (often more three orders magnitude faster), can scale larger graphs methods.