Subgraph Discovery in Information Networks

Top-K Interesting Subgraph Discovery in Information Networks Report Title In the real world, various systems can be modeled using heterogeneous networks which consist of entities of different types. Many problems on such networks can be mapped to an underlying critical problem of discovering topK subgraphs of entities with rare and surprising associations. Answering such subgraph queries efficiently involves two main challenges: (1) computing all matching subgraphs which satisfy the query and (2) ranking such results based on the rarity and the interestingness of the associations among entities in the subgraphs. Previous work on the matching problem can be harnessed for a na ̈?ve ranking-after-matching solution. However, for large graphs, subgraph queries may have enormous number of matches, and so it is inefficient to compute all matches when only the top-K matches are desired. In this paper, we address the two challenges of matching and ranking in top-K subgraph discovery as follows. First, we introduce two index structures for the network: topology index, and graph maximum metapath weight index, which are both computed offline. Second, we propose novel top-K mechanisms to exploit these indexes for answering interesting subgraph queries online efficiently. Experimental results on several synthetic datasets and the DBLP and Wikipedia datasets containing thousands of entities show the efficiency and the effectiveness of the proposed approach in computing interesting subgraphs. Conference Name: Proc. 2014 IEEE Int. Conf. on Data Engineering (ICDE'14), Chicago, IL, Mar. 2014 Conference Date: March 03, 2014 Top-K Interesting Subgraph Discovery in Information Networks Manish Gupta, Jing Gao, Xifeng Yan, Hasan Cam and Jiawei Han ¶ ∗Microsoft, India. Email: gmanish@microsoft.com †State University of New York at Buffalo. Email: jing@buffalo.edu ‡University of California, Santa Barbara. Email: xyan@cs.ucsb.edu §US Army Research Lab. Email: hasan.cam.civ@mail.mil ¶University of Illinois at Urbana-Champaign. Email: hanj@cs.uiuc.edu Abstract—In the real world, various systems can be modeled using heterogeneous networks which consist of entities of different types. Many problems on such networks can be mapped to an underlying critical problem of discovering topK subgraphs of entities with rare and surprising associations. Answering such subgraph queries efficiently involves two main challenges: (1) computing allmatching subgraphs which satisfy the query and (2) ranking such results based on the rarity and the interestingness of the associations among entities in the subgraphs. Previous work on the matching problem can be harnessed for a näıve ranking-after-matching solution. However, for large graphs, subgraph queries may have enormous number of matches, and so it is inefficient to compute all matches when only the top-K matches are desired. In this paper, we address the two challenges of matching and ranking in top-K subgraph discovery as follows. First, we introduce two index structures for the network: topology index, and graph maximum metapath weight index, which are both computed offline. Second, we propose novel top-Kmechanisms to exploit these indexes for answering interesting subgraph queries online efficiently. Experimental results on several synthetic datasets and the DBLP and Wikipedia datasets containing thousands of entities show the efficiency and the effectiveness of the proposed approach in computing interesting subgraphs.In the real world, various systems can be modeled using heterogeneous networks which consist of entities of different types. Many problems on such networks can be mapped to an underlying critical problem of discovering topK subgraphs of entities with rare and surprising associations. Answering such subgraph queries efficiently involves two main challenges: (1) computing allmatching subgraphs which satisfy the query and (2) ranking such results based on the rarity and the interestingness of the associations among entities in the subgraphs. Previous work on the matching problem can be harnessed for a näıve ranking-after-matching solution. However, for large graphs, subgraph queries may have enormous number of matches, and so it is inefficient to compute all matches when only the top-K matches are desired. In this paper, we address the two challenges of matching and ranking in top-K subgraph discovery as follows. First, we introduce two index structures for the network: topology index, and graph maximum metapath weight index, which are both computed offline. Second, we propose novel top-Kmechanisms to exploit these indexes for answering interesting subgraph queries online efficiently. Experimental results on several synthetic datasets and the DBLP and Wikipedia datasets containing thousands of entities show the efficiency and the effectiveness of the proposed approach in computing interesting subgraphs.