document: Center-piece subgraphs : problem definition and fast solutions Given Q nodes in a social network ( say , authorship network ) , how can we find the node\/author that is the center-piece , and has direct or indirect connections to all , or most of them ? For example , this node could be the common advisor , or someone who started the research area that the Q nodes belong to . Isomorphic scenarios appear in law enforcement ( find the master-mind criminal , connected to all current suspects ) , gene regulatory networks ( find the protein that participates in pathways with all or most of the given Q proteins ) , viral marketing and many more . Connection subgraphs is an important first step , handling the case of Q = 2 query nodes . Then , the connection subgraph algorithm finds the b intermediate nodes , that provide a good connection between the two original query nodes . Here we generalize the challenge in multiple dimensions : First , we allow more than two query nodes . Second , we allow a whole family of queries , ranging from ` OR ' to ` AND ' , with ` softAND ' in-between . Finally , we design and compare a fast approximation , and study the quality\/speed trade-off . We also present experiments on the DBLP dataset . The experiments confirm that our proposed method naturally deals with multi-source queries and that the resulting subgraphs agree with our intuition . Wall-clock timing results on the DBLP dataset show that our proposed approximation achieve good accuracy for about 6:1 speedup .
doc_bio_tags: O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O