Campus Units
Computer Science, Electrical and Computer Engineering
Document Type
Article
Publication Version
Submitted Manuscript
Publication Date
2018
Journal or Book Title
arXiv
Abstract
Quasi-cliques are dense incomplete subgraphs of a graph that generalize the notion of cliques. Enumerating quasi-cliques from a graph is a robust way to detect densely connected structures with applications to bio-informatics and social network analysis. However, enumerating quasi-cliques in a graph is a challenging problem, even harder than the problem of enumerating cliques. We consider the enumeration of top-k degree-based quasi-cliques, and make the following contributions: (1) We show that even the problem of detecting if a given quasi-clique is maximal (i.e. not contained within another quasi-clique) is NP-hard (2) We present a novel heuristic algorithm KernelQC to enumerate the k largest quasi-cliques in a graph. Our method is based on identifying kernels of extremely dense subgraphs within a graph, following by growing subgraphs around these kernels, to arrive at quasi-cliques with the required densities (3) Experimental results show that our algorithm accurately enumerates quasi-cliques from a graph, is much faster than current state-of-the-art methods for quasi-clique enumeration (often more than three orders of magnitude faster), and can scale to larger graphs than current methods.
Copyright Owner
The Authors
Copyright Date
2018
Language
en
File Format
application/pdf
Recommended Citation
Sanei-Mehri, Seyed-Vahid; Das, Apurba; and Tirthapura, Srikanta, "Enumerating Top-k Quasi-Cliques" (2018). Electrical and Computer Engineering Publications. 198.
https://lib.dr.iastate.edu/ece_pubs/198
Comments
This is a pre-print of the article Sanei-Mehri, Seyed-Vahid, Apurba Das, and Srikanta Tirthapura. "Enumerating Top-k Quasi-Cliques." arXiv preprint arXiv:1808.09531 (2018). Posted with permission.