TY - GEN
T1 - G-Finder
T2 - 2019 IEEE International Conference on Big Data, Big Data 2019
AU - Liu, Lihui
AU - Du, Boxin
AU - Xu, Jiejun
AU - Tong, Hanghang
PY - 2019/12
Y1 - 2019/12
N2 - Subgraph matching is a core primitive across a number of disciplines, ranging from data mining, databases, information retrieval, computer vision to natural language processing. Despite decades of efforts, it is still highly challenging to balance between the matching accuracy and the computational efficiency, especially when the query graph and/or the data graph are large. In this paper, we propose an index-based algorithm (G-FINDER) to find the top-k approximate matching subgraphs. At the heart of the proposed algorithm are two techniques, including (1) a novel auxiliary data structure (LOOKUP-TABLE) in conjunction with a neighborhood expansion method to effectively and efficiently index candidate vertices, and (2) a dynamic filtering and refinement strategy to prune the false candidates at an early stage. The proposed G-FINDER bears some distinctive features, including (1) generality, being able to handle different types of inexact matching (e.g., missing nodes, missing edges, intermediate vertices) on node attributed and/or edge attributed graphs or multigraphs; (2) effectiveness, achieving up to 30% Fl-Score improvement over the best known competitor; and (3) efficiency, scaling near-linearly w.r.t. the size of the data graph as well as the query graph.
AB - Subgraph matching is a core primitive across a number of disciplines, ranging from data mining, databases, information retrieval, computer vision to natural language processing. Despite decades of efforts, it is still highly challenging to balance between the matching accuracy and the computational efficiency, especially when the query graph and/or the data graph are large. In this paper, we propose an index-based algorithm (G-FINDER) to find the top-k approximate matching subgraphs. At the heart of the proposed algorithm are two techniques, including (1) a novel auxiliary data structure (LOOKUP-TABLE) in conjunction with a neighborhood expansion method to effectively and efficiently index candidate vertices, and (2) a dynamic filtering and refinement strategy to prune the false candidates at an early stage. The proposed G-FINDER bears some distinctive features, including (1) generality, being able to handle different types of inexact matching (e.g., missing nodes, missing edges, intermediate vertices) on node attributed and/or edge attributed graphs or multigraphs; (2) effectiveness, achieving up to 30% Fl-Score improvement over the best known competitor; and (3) efficiency, scaling near-linearly w.r.t. the size of the data graph as well as the query graph.
KW - approximate matching
KW - subgraph index
KW - subgraph matching
UR - http://www.scopus.com/inward/record.url?scp=85081290751&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85081290751&partnerID=8YFLogxK
U2 - 10.1109/BigData47090.2019.9006525
DO - 10.1109/BigData47090.2019.9006525
M3 - Conference contribution
T3 - Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019
SP - 513
EP - 522
BT - Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019
A2 - Baru, Chaitanya
A2 - Huan, Jun
A2 - Khan, Latifur
A2 - Hu, Xiaohua Tony
A2 - Ak, Ronay
A2 - Tian, Yuanyuan
A2 - Barga, Roger
A2 - Zaniolo, Carlo
A2 - Lee, Kisung
A2 - Ye, Yanfang Fanny
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 9 December 2019 through 12 December 2019
ER -