TY - GEN
T1 - Query-driven discovery of semantically similar substructures in heterogeneous networks
AU - Yu, Xiao
AU - Sun, Yizhou
AU - Zhao, Peixiang
AU - Han, Jiawei
PY - 2012
Y1 - 2012
N2 - Heterogeneous information networks that contain multiple types of objects and links are ubiquitous in the real world, such as bibliographic networks, cyber-physical networks, and social media networks. Although researchers have studied various data mining tasks in information networks, interactive query-based network exploration techniques have not been addressed systematically, which, in fact, are highly desirable for exploring large-scale information networks. In this demo, we introduce and demonstrate our recent research project on query-driven discovery of semantically similar substructures in heterogeneous networks. Given a subgraph query, our system searches a given large information network and finds efficiently a list of subgraphs that are structurally identical and semantically similar. Since data mining methods are used to obtain semantically similar entities (nodes), we use discovery as a term to describe this process. In order to achieve high efficiency and scalability, we design and implement a filter-and verification search framework, which can first generate promising subgraph candidates using off line indices built by data mining results, and then verify candidates with a recursive pruning matching process. The proposed system demonstrates the effectiveness of our query-driven semantic similarity search framework and the efficiency of the proposed methodology on multiple real-world heterogeneous information networks.
AB - Heterogeneous information networks that contain multiple types of objects and links are ubiquitous in the real world, such as bibliographic networks, cyber-physical networks, and social media networks. Although researchers have studied various data mining tasks in information networks, interactive query-based network exploration techniques have not been addressed systematically, which, in fact, are highly desirable for exploring large-scale information networks. In this demo, we introduce and demonstrate our recent research project on query-driven discovery of semantically similar substructures in heterogeneous networks. Given a subgraph query, our system searches a given large information network and finds efficiently a list of subgraphs that are structurally identical and semantically similar. Since data mining methods are used to obtain semantically similar entities (nodes), we use discovery as a term to describe this process. In order to achieve high efficiency and scalability, we design and implement a filter-and verification search framework, which can first generate promising subgraph candidates using off line indices built by data mining results, and then verify candidates with a recursive pruning matching process. The proposed system demonstrates the effectiveness of our query-driven semantic similarity search framework and the efficiency of the proposed methodology on multiple real-world heterogeneous information networks.
KW - data mining
KW - graph query
KW - information network
UR - http://www.scopus.com/inward/record.url?scp=84866009688&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84866009688&partnerID=8YFLogxK
U2 - 10.1145/2339530.2339765
DO - 10.1145/2339530.2339765
M3 - Conference contribution
AN - SCOPUS:84866009688
SN - 9781450314626
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 1500
EP - 1503
BT - KDD'12 - 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
T2 - 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2012
Y2 - 12 August 2012 through 16 August 2012
ER -