 Mining maximal\lceil \rceil  quasi-cliques is notoriously expensive with the state-of-the-art algorithm scaling only to small graphs with thousands of vertices.This has hampered its popularity in real applications involving big graphs. We developed a task-based system called G-thinker for massively parallel graph mining, which is the first graph mining system that scales with the number of CPU cores. G-thinker provides a unique opportunity to scale the compute-intensive quasi-clique mining . This paperdesigns parallel algorithms for mining maximal quasi-cliques on G-thinker that scale to big graphs.
<coherence>  Mining maximal\lceil \rceil  quasi-cliques is notoriously expensive with the state-of-the-art algorithm scaling only to small graphs with thousands of vertices.This has hampered its popularity in real applications involving big graphs. We developed a task-based system called G-thinker for massively parallel graph mining, which is the first graph mining system that scales with the number of CPU cores. G-thinker provides a unique opportunity to scale the compute-intensive quasi-clique mining . This paperdesigns parallel algorithms for mining maximal quasi-cliques on G-thinker that scale to big graphs.
 Mining maximal\lceil \rceil quasi-cliques is notoriously expensive. In this paper, we design parallel algorithms for mining maximal quasi-cliques on G-thinker that scale to big graphs.