skip to main content
10.1145/3366424.3382688acmconferencesArticle/Chapter ViewAbstractPublication PagesthewebconfConference Proceedingsconference-collections
research-article

From Closing Triangles to Closing Higher-Order Motifs

Published: 20 April 2020 Publication History

Abstract

This work introduces higher-order ranking and link prediction methods based on closing higher-order network motifs. In particular, we propose the general notion of a motif closure that goes beyond simple triangle closures and demonstrate that these new motif closures often outperform triangle-based methods. This result implies that one should consider other motif closures beyond simple triangles. We also find that the “best” motif closure depends highly on the underlying network and its structural properties. Furthermore, the methods are fast and efficient for real-time applications such as online visitor stitching, web search, and recommendation. The experimental results indicate the importance of these new motif closures. Finally, the new motif closures can serve as a basis for developing better (un)supervised ranking/link prediction methods.

References

[1]
Nesreen K. Ahmed, Jennifer Neville, Ryan A. Rossi, and Nick Duffield. 2015. Efficient Graphlet Counting for Large Networks. In ICDM. 10.
[2]
Mohammad Al Hasan, Vineet Chaoji, Saeed Salem, and Mohammed Zaki. 2006. Link prediction using supervised learning. In SDM Workshop.
[3]
Ryan N Lichtenwalter, Jake T Lussier, and Nitesh V Chawla. 2010. New perspectives and methods in link prediction. In KDD. 243–252.
[4]
Ryan A. Rossi, Rong Zhou, and Nesreen K. Ahmed. 2018. Estimation of Graphlet Counts in Massive Networks. In TNNLS. 44–57.
[5]
David H Wolpert, William G Macready, 1997. No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1, 1(1997), 67–82.

Cited By

View all
  • (2023)MOSER: Scalable Network Motif Discovery Using Serial TestProceedings of the VLDB Endowment10.14778/3632093.363211817:3(591-603)Online publication date: 1-Nov-2023
  • (2023)Hyperedge prediction and the statistical mechanisms of higher-order and lower-order interactions in complex networksProceedings of the National Academy of Sciences10.1073/pnas.2303887120120:50Online publication date: 7-Dec-2023
  • (2022)Motif Cut Sparsifiers2022 IEEE 63rd Annual Symposium on Foundations of Computer Science (FOCS)10.1109/FOCS54457.2022.00044(389-398)Online publication date: Oct-2022
  • Show More Cited By

Index Terms

  1. From Closing Triangles to Closing Higher-Order Motifs
          Index terms have been assigned to the content through auto-classification.

          Recommendations

          Comments

          Information & Contributors

          Information

          Published In

          cover image ACM Conferences
          WWW '20: Companion Proceedings of the Web Conference 2020
          April 2020
          854 pages
          ISBN:9781450370240
          DOI:10.1145/3366424
          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]

          Sponsors

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          Published: 20 April 2020

          Permissions

          Request permissions for this article.

          Check for updates

          Qualifiers

          • Research-article
          • Research
          • Refereed limited

          Conference

          WWW '20
          Sponsor:
          WWW '20: The Web Conference 2020
          April 20 - 24, 2020
          Taipei, Taiwan

          Acceptance Rates

          Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

          Contributors

          Other Metrics

          Bibliometrics & Citations

          Bibliometrics

          Article Metrics

          • Downloads (Last 12 months)13
          • Downloads (Last 6 weeks)0
          Reflects downloads up to 15 Feb 2025

          Other Metrics

          Citations

          Cited By

          View all
          • (2023)MOSER: Scalable Network Motif Discovery Using Serial TestProceedings of the VLDB Endowment10.14778/3632093.363211817:3(591-603)Online publication date: 1-Nov-2023
          • (2023)Hyperedge prediction and the statistical mechanisms of higher-order and lower-order interactions in complex networksProceedings of the National Academy of Sciences10.1073/pnas.2303887120120:50Online publication date: 7-Dec-2023
          • (2022)Motif Cut Sparsifiers2022 IEEE 63rd Annual Symposium on Foundations of Computer Science (FOCS)10.1109/FOCS54457.2022.00044(389-398)Online publication date: Oct-2022
          • (2021)Joint Subgraph-to-Subgraph Transitions: Generalizing Triadic Closure for Powerful and Interpretable Graph ModelingProceedings of the 14th ACM International Conference on Web Search and Data Mining10.1145/3437963.3441817(815-823)Online publication date: 8-Mar-2021
          • (2021)A Framework for Knowledge-Derived Query Suggestions2021 IEEE International Conference on Big Data (Big Data)10.1109/BigData52589.2021.9671344(510-518)Online publication date: 15-Dec-2021

          View Options

          Login options

          View options

          PDF

          View or Download as a PDF file.

          PDF

          eReader

          View online with eReader.

          eReader

          HTML Format

          View this article in HTML Format.

          HTML Format

          Figures

          Tables

          Media

          Share

          Share

          Share this Publication link

          Share on social media