 This paperdesigns parallel algorithms for mining maximal quasi-cliques on G-thinker that scale to big graphs. Our algorithms follow the idea of divide and conquer which partitions the problem of mining a big graph into tasks that mine smaller subgraphs .
<meaning-changed>  This paperdesigns parallel algorithms for mining maximal quasi-cliques on G-thinker that scale to big graphs. Our algorithms follow the idea of divide and conquer which partitions the problem of mining a big graph into tasks that mine smaller subgraphs .
 This paperdesigns parallel algorithms for mining maximal quasi-cliques on G-thinker , a recent distributed framework targeting divide-and-conquer graph mining algorithms that decomposes the mining into compute-intensive tasks to fully utilize CPU cores .