research-article

Growing Graphs from Hyperedge Replacement Graph Grammars

Authors:

Salvador Aguiñaga,

Rodrigo Palacios,

Tim WeningerAuthors Info & Claims

CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

Pages 469 - 478

https://doi.org/10.1145/2983323.2983826

Published: 24 October 2016 Publication History

Abstract

Discovering the underlying structures present in large real world graphs is a fundamental scientific problem. In this paper we show that a graph's clique tree can be used to extract a hyperedge replacement grammar. If we store an ordering from the extraction process, the extracted graph grammar is guaranteed to generate an isomorphic copy of the original graph. Or, a stochastic application of the graph grammar rules can be used to quickly create random graphs. In experiments on large real world networks, we show that random graphs, generated from extracted graph grammars, exhibit a wide range of properties that are very similar to the original graphs. In addition to graph properties like degree or eigenvector centrality, what a graph ``looks like'' ultimately depends on small details in local graph substructures that are difficult to define at a global level. We show that our generative graph model is able to preserve these local substructures when generating new graphs and performs well on new and difficult tests of model robustness.

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Cited By

Sikdar SCedre DFord TWeninger T(2023)The Infinity Mirror Test for Graph ModelsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.314025235:4(4281-4292)Online publication date: 1-Apr-2023
https://doi.org/10.1109/TKDE.2022.3140252
Aguinaga SChiang DWeninger T(2019)Learning Hyperedge Replacement Grammars for Graph GenerationIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2018.281087741:3(625-638)Online publication date: 1-Mar-2019
https://dl.acm.org/doi/10.1109/TPAMI.2018.2810877
Sikdar SHibshman JWeninger T(2019)Modeling Graphs with Vertex Replacement Grammars2019 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM.2019.00066(558-567)Online publication date: Nov-2019
https://doi.org/10.1109/ICDM.2019.00066
Show More Cited By

Index Terms

Growing Graphs from Hyperedge Replacement Graph Grammars
1. Mathematics of computing
  1. Discrete mathematics
    1. Graph theory
      1. Graph algorithms
      2. Hypergraphs
2. Theory of computation
  1. Randomness, geometry and discrete structures
    1. Random network models

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cover image ACM Conferences

CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

October 2016

2566 pages

ISBN:9781450340731

DOI:10.1145/2983323

General Chairs:
Snehasis Mukhopadhyay
Indiana University Purdue University Indianapolis, USA
,
ChengXiang Zhai
University of Illinois at Urbana-Champaign, USA
,
Program Chairs:
Elisa Bertino
Purdue University
,
Fabio Crestani
University of Lugano
,
Javed Mostafa
University of North Carolina
,
Jie Tang
Tsinghua University
,
Luo Si
Alibaba Group Inc & Purdue University
,
Xiaofang Zhou
University of Queensland
,
Yi Chang
Yahoo Research
,
Yunyao Li
IBM Research - Almaden
,
Parikshit Sondhi
WalmartLabs

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 the author(s) 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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Publication History

Published: 24 October 2016

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John Templeton Foundation

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CIKM'16: ACM Conference on Information and Knowledge Management

October 24 - 28, 2016

Indiana, Indianapolis, USA

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CIKM '16 Paper Acceptance Rate 160 of 701 submissions, 23%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Sikdar SCedre DFord TWeninger T(2023)The Infinity Mirror Test for Graph ModelsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.314025235:4(4281-4292)Online publication date: 1-Apr-2023
https://doi.org/10.1109/TKDE.2022.3140252
Aguinaga SChiang DWeninger T(2019)Learning Hyperedge Replacement Grammars for Graph GenerationIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2018.281087741:3(625-638)Online publication date: 1-Mar-2019
https://dl.acm.org/doi/10.1109/TPAMI.2018.2810877
Sikdar SHibshman JWeninger T(2019)Modeling Graphs with Vertex Replacement Grammars2019 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM.2019.00066(558-567)Online publication date: Nov-2019
https://doi.org/10.1109/ICDM.2019.00066
Hibshman JSikdar SWeninger T(2019)Towards Interpretable Graph Modeling with Vertex Replacement Grammars2019 IEEE International Conference on Big Data (Big Data)10.1109/BigData47090.2019.9006204(770-779)Online publication date: Dec-2019
https://doi.org/10.1109/BigData47090.2019.9006204
Pennycuff CSikdar SVajiac CChiang DWeninger T(2018)Synchronous Hyperedge Replacement Graph GrammarsGraph Transformation10.1007/978-3-319-92991-0_2(20-36)Online publication date: 29-May-2018
https://doi.org/10.1007/978-3-319-92991-0_2

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