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Adding Counting Quantifiers to Graph Patterns

Authors:

Jingbo XuAuthors Info & Claims

SIGMOD '16: Proceedings of the 2016 International Conference on Management of Data

Pages 1215 - 1230

https://doi.org/10.1145/2882903.2882937

Published: 14 June 2016 Publication History

Abstract

This paper proposes quantified graph patterns (QGPs), an extension of graph patterns by supporting simple counting quantifiers on edges. We show that QGPs naturally express universal and existential quantification, numeric and ratio aggregates, as well as negation. Better still, the increased expressivity does not come with a much higher price. We show that quantified matching, i.e., graph pattern matching with QGPs, remains NP-complete in the absence of negation, and is DP-complete for general QGPs. We show how quantified matching can be conducted by incorporating quantifier checking into conventional subgraph isomorphism methods. We also develop parallel scalable algorithms for quantified matching. As an application of QGPs, we introduce quantified graph association rules defined with QGPs, to identify potential customers in social media marketing. Using real-life and synthetic graphs, we experimentally verify the effectiveness of QGPs and the scalability of our algorithms.

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Cited By

Mishina HUeda K(2024)Introducing Quantification into a Hierarchical Graph Rewriting LanguageLogic-Based Program Synthesis and Transformation10.1007/978-3-031-71294-4_13(220-239)Online publication date: 9-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-71294-4_13
Zeng JU LYan XLi YHan MTang B(2023)Extracting Top-$k$ Frequent and Diversified Patterns in Knowledge GraphsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.3233594(1-18)Online publication date: 2023
https://doi.org/10.1109/TKDE.2022.3233594
Jiang PWang WLiu XSun LDong B(2023)Event Association Analysis Using Graph RulesArtificial Neural Networks and Machine Learning – ICANN 202310.1007/978-3-031-44216-2_29(352-363)Online publication date: 22-Sep-2023
https://doi.org/10.1007/978-3-031-44216-2_29
Show More Cited By

Index Terms

Adding Counting Quantifiers to Graph Patterns
1. Theory of computation
  1. Design and analysis of algorithms
    1. Data structures design and analysis
      1. Pattern matching

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cover image ACM Conferences

SIGMOD '16: Proceedings of the 2016 International Conference on Management of Data

June 2016

2300 pages

ISBN:9781450335317

DOI:10.1145/2882903

General Chairs:
Fatma Özcan
IBM Research, USA
,
Georgia Koutrika
HP Labs, USA
,
Program Chair:
Sam Madden
Massachusetts Institute of Technology, USA

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 14 June 2016

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Research-article

Funding Sources

National Natural Science Foundation of China
European Research Council
Shenzhen Science and Technology Fund
973 Program
Guangdong Applied R&D Program
Engineering and Physical Sciences Research Council
National Science Foundation
Shenzhen Peacock Program
Guangdong Innovative Research Team Program

Conference

SIGMOD/PODS'16

Sponsor:

SIGMOD

SIGMOD/PODS'16: International Conference on Management of Data

June 26 - July 1, 2016

California, San Francisco, USA

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Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Citations

Cited By

Mishina HUeda K(2024)Introducing Quantification into a Hierarchical Graph Rewriting LanguageLogic-Based Program Synthesis and Transformation10.1007/978-3-031-71294-4_13(220-239)Online publication date: 9-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-71294-4_13
Zeng JU LYan XLi YHan MTang B(2023)Extracting Top-$k$ Frequent and Diversified Patterns in Knowledge GraphsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.3233594(1-18)Online publication date: 2023
https://doi.org/10.1109/TKDE.2022.3233594
Jiang PWang WLiu XSun LDong B(2023)Event Association Analysis Using Graph RulesArtificial Neural Networks and Machine Learning – ICANN 202310.1007/978-3-031-44216-2_29(352-363)Online publication date: 22-Sep-2023
https://doi.org/10.1007/978-3-031-44216-2_29
Bonifati ADumbrava SFletcher GHidders JHofer MMartens WMurlak FShinavier JStaworko STomaszuk D(2022)Threshold queries in theory and in the wildProceedings of the VLDB Endowment10.14778/3510397.351040715:5(1105-1118)Online publication date: 18-May-2022
https://dl.acm.org/doi/10.14778/3510397.3510407
He YWang XSha YZhong XFang Y(2022)Mining Frequent Patterns with Counting QuantifiersWeb and Big Data10.1007/978-3-031-25158-0_28(372-381)Online publication date: 11-Aug-2022
https://dl.acm.org/doi/10.1007/978-3-031-25158-0_28
Ma ZLi GMa RMa ZLi GMa R(2022)Fuzzy RDF QueriesModeling and Management of Fuzzy Semantic RDF Data10.1007/978-3-031-11669-8_5(151-207)Online publication date: 9-Sep-2022
https://doi.org/10.1007/978-3-031-11669-8_5
Sun GLiu GWang YOrgun MSheng QZhou X(2021)Partition-Aware Graph Pattern based Node Matching with UpdatesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.3103914(1-1)Online publication date: 2021
https://doi.org/10.1109/TKDE.2021.3103914
Sun GLiu GWang YOrgun MSheng QZhou X(2021)Incremental Graph Pattern Based Node Matching with Multiple UpdatesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.294229433:4(1585-1600)Online publication date: 1-Apr-2021
https://doi.org/10.1109/TKDE.2019.2942294
Zeng JU LYan XHan MTang B(2021)Fast Core-based Top-k Frequent Pattern Discovery in Knowledge Graphs2021 IEEE 37th International Conference on Data Engineering (ICDE)10.1109/ICDE51399.2021.00086(936-947)Online publication date: Apr-2021
https://doi.org/10.1109/ICDE51399.2021.00086
Mahfoud H(2021)Expressive top-k matching for conditional graph patternsNeural Computing and Applications10.1007/s00521-021-06590-734:17(14205-14221)Online publication date: 29-Oct-2021
https://dl.acm.org/doi/10.1007/s00521-021-06590-7
Show More Cited By

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