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Fast and Accurate Estimation of Typed Graphlets

Published: 20 April 2020 Publication History

Abstract

Typed graphlets are small typed (labeled, colored) induced subgraphs and were recently shown to be the fundamental building blocks of rich complex heterogeneous networks. In many applications, speed is more important than accuracy, and it is sufficient to trade-off a tiny amount of accuracy for a significantly faster method. In this work, we propose fast and accurate estimators for typed graphlets. The typed graphlet estimation techniques naturally support general heterogeneous graphs with any arbitrary number of types, which include bipartite, k-partite, k-star, labeled graphs, and attributed networks as special cases. The experiments demonstrate the effectiveness of the typed graphlet estimation techniques.

References

[1]
Aldo G. Carranza, Ryan A. Rossi, Anup Rao, and Eunyee Koh. 2018. Higher-order Spectral Clustering for Heterogeneous Graphs. In arXiv:1810.02959. 15.
[2]
Madhav Jha, C Seshadhri, and Ali Pinar. 2015. Path sampling: A fast and provable method for estimating 4-vertex subgraph counts. In WWW. 495–505.
[3]
Ryan A. Rossi, Nesreen K. Ahmed, Aldo Carranza, David Arbour, Anup Rao, Sungchul Kim, and Eunyee Koh. 2019. Heterogeneous Network Motifs. In arXiv:1901.10026. 18.

Cited By

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  • (2023)Structure Learning Via Meta-Hyperedge for Dynamic Rumor DetectionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.322143835:9(9128-9139)Online publication date: 1-Sep-2023
  • (2020)Higher-order Clustering in Complex Heterogeneous NetworksProceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3394486.3403045(25-35)Online publication date: 23-Aug-2020

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    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]

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 20 April 2020

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

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

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    View all
    • (2023)Structure Learning Via Meta-Hyperedge for Dynamic Rumor DetectionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.322143835:9(9128-9139)Online publication date: 1-Sep-2023
    • (2020)Higher-order Clustering in Complex Heterogeneous NetworksProceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3394486.3403045(25-35)Online publication date: 23-Aug-2020

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