IEICE TRANSACTIONS on Information
Efficient Supergraph Search Using Graph Coding
Summary :
This paper proposes a method for searching for graphs in the database which are contained as subgraphs by a given query. In the proposed method, the search index does not require any knowledge of the query set or the frequent subgraph patterns. In conventional techniques, enumerating and selecting frequent subgraph patterns is computationally expensive, and the distribution of the query set must be known in advance. Subsequent changes to the query set require the frequent patterns to be selected again and the index to be reconstructed. The proposed method overcomes these difficulties through graph coding, using a tree structured index that contains infrequent subgraph patterns in the shallow part of the tree. By traversing this code tree, we are able to rapidly determine whether multiple graphs in the database contain subgraphs that match the query, producing a powerful pruning or filtering effect. Furthermore, the filtering and verification steps of the graph search can be conducted concurrently, rather than requiring separate algorithms. As the proposed method does not require the frequent subgraph patterns and the query set, it is significantly faster than previous techniques; this independence from the query set also means that there is no need to reconstruct the search index when the query set changes. A series of experiments using a real-world dataset demonstrate the efficiency of the proposed method, achieving a search speed several orders of magnitude faster than the previous best.
- Publication
- IEICE TRANSACTIONS on Information Vol.E103-D No.1 pp.130-141
- Publication Date
- 2020/01/01
- Publicized
- 2019/09/26
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2019EDP7011
- Type of Manuscript
- PAPER
- Category
- Data Engineering, Web Information Systems
Authors
Shun IMAI
Kwansei Gakuin University
Akihiro INOKUCHI
Kwansei Gakuin University
Keyword
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Shun IMAI, Akihiro INOKUCHI, "Efficient Supergraph Search Using Graph Coding" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 1, pp. 130-141, January 2020, doi: 10.1587/transinf.2019EDP7011.
Abstract: This paper proposes a method for searching for graphs in the database which are contained as subgraphs by a given query. In the proposed method, the search index does not require any knowledge of the query set or the frequent subgraph patterns. In conventional techniques, enumerating and selecting frequent subgraph patterns is computationally expensive, and the distribution of the query set must be known in advance. Subsequent changes to the query set require the frequent patterns to be selected again and the index to be reconstructed. The proposed method overcomes these difficulties through graph coding, using a tree structured index that contains infrequent subgraph patterns in the shallow part of the tree. By traversing this code tree, we are able to rapidly determine whether multiple graphs in the database contain subgraphs that match the query, producing a powerful pruning or filtering effect. Furthermore, the filtering and verification steps of the graph search can be conducted concurrently, rather than requiring separate algorithms. As the proposed method does not require the frequent subgraph patterns and the query set, it is significantly faster than previous techniques; this independence from the query set also means that there is no need to reconstruct the search index when the query set changes. A series of experiments using a real-world dataset demonstrate the efficiency of the proposed method, achieving a search speed several orders of magnitude faster than the previous best.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019EDP7011/_p
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@ARTICLE{e103-d_1_130,
author={Shun IMAI, Akihiro INOKUCHI, },
journal={IEICE TRANSACTIONS on Information},
title={Efficient Supergraph Search Using Graph Coding},
year={2020},
volume={E103-D},
number={1},
pages={130-141},
abstract={This paper proposes a method for searching for graphs in the database which are contained as subgraphs by a given query. In the proposed method, the search index does not require any knowledge of the query set or the frequent subgraph patterns. In conventional techniques, enumerating and selecting frequent subgraph patterns is computationally expensive, and the distribution of the query set must be known in advance. Subsequent changes to the query set require the frequent patterns to be selected again and the index to be reconstructed. The proposed method overcomes these difficulties through graph coding, using a tree structured index that contains infrequent subgraph patterns in the shallow part of the tree. By traversing this code tree, we are able to rapidly determine whether multiple graphs in the database contain subgraphs that match the query, producing a powerful pruning or filtering effect. Furthermore, the filtering and verification steps of the graph search can be conducted concurrently, rather than requiring separate algorithms. As the proposed method does not require the frequent subgraph patterns and the query set, it is significantly faster than previous techniques; this independence from the query set also means that there is no need to reconstruct the search index when the query set changes. A series of experiments using a real-world dataset demonstrate the efficiency of the proposed method, achieving a search speed several orders of magnitude faster than the previous best.},
keywords={},
doi={10.1587/transinf.2019EDP7011},
ISSN={1745-1361},
month={January},}
Copy
TY - JOUR
TI - Efficient Supergraph Search Using Graph Coding
T2 - IEICE TRANSACTIONS on Information
SP - 130
EP - 141
AU - Shun IMAI
AU - Akihiro INOKUCHI
PY - 2020
DO - 10.1587/transinf.2019EDP7011
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2020
AB - This paper proposes a method for searching for graphs in the database which are contained as subgraphs by a given query. In the proposed method, the search index does not require any knowledge of the query set or the frequent subgraph patterns. In conventional techniques, enumerating and selecting frequent subgraph patterns is computationally expensive, and the distribution of the query set must be known in advance. Subsequent changes to the query set require the frequent patterns to be selected again and the index to be reconstructed. The proposed method overcomes these difficulties through graph coding, using a tree structured index that contains infrequent subgraph patterns in the shallow part of the tree. By traversing this code tree, we are able to rapidly determine whether multiple graphs in the database contain subgraphs that match the query, producing a powerful pruning or filtering effect. Furthermore, the filtering and verification steps of the graph search can be conducted concurrently, rather than requiring separate algorithms. As the proposed method does not require the frequent subgraph patterns and the query set, it is significantly faster than previous techniques; this independence from the query set also means that there is no need to reconstruct the search index when the query set changes. A series of experiments using a real-world dataset demonstrate the efficiency of the proposed method, achieving a search speed several orders of magnitude faster than the previous best.
ER -
English
