Abstract
AbstractA clique is a complete subgraph of a graph. Often, a clique is interpreted as a dense module of vertices within a graph. However, in many real-world situations, the classical problem of finding a clique is required to be relaxed. This motivates the problem of finding quasicliques that are almost complete subgraphs of a graph. In sparse and very large scale-free networks, the problem of finding the largest quasi-clique becomes hard to manage with the existing approaches. Here, we propose a heuristic algorithm in this paper for locating the largest quasi-clique from the human protein-protein interaction networks. The results show promise in computational biology research by the exploration of significant protein modules.
Highlights
Total number of proteins in Homo Sapiens is in the order of 105, whereas the interactome size is as low as 0.0002% [1]
Joint mining of different types of networks for exploring quasi-cliques. Time complexity of this algorithm linearly grows with the number of quasi-cliques
Definition of a quasi-clique based on total number of edges and individual degrees
Summary
Total number of proteins in Homo Sapiens is in the order of 105, whereas the interactome size is as low as 0.0002% [1]. A graph G = (V, E) of degree at least r is γ-quasi-complete for γ ≤ r/(|V|-1). An acyclic graph G = (V, E) of order at least 2 is (|V|-1)-1quasi-complete
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
