A fast method for detecting minority structures in a graph
Authors: 
Fangfang Zhou, Qi'an Chen, Yunlong Cui, Xinyuan Wang, Hongxu Ma, Ying Zhao, Xiaoli LiAuthors Info & Claims
Fangfang Zhou, Qi'an Chen, Yunlong Cui, Xinyuan Wang, Hongxu Ma, Ying Zhao, Xiaoli LiAuthors Info & ClaimsArticle No.: 26, Pages 1 - 2
Abstract
A graph contains plentiful structures. Some minority structures are important, such as high degree nodes and bridges. Detecting these minority structures is beneficial to accelerate computational graph analysis and improve the comprehension of graph visualization. Regarding four typical minority structures, this paper proposes two algorithms to detect these structures fast and efficiently. A set of experiments demonstrate the effectiveness of the proposed algorithms.
Reference
[1]
Y. Zhao, H. Jiang, Q. Chen, Y. Qin, H. Xie, Y. Wu, S. Liu, Z. Zhou, J. Xia, and F. Zhou. Preserving Minority Structures in Graph Sampling. IEEE Transactions on Visualization and Computer Graphics, 28(2):1--10, 2021.
Index Terms
- A fast method for detecting minority structures in a graph
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Information & Contributors
Information
Published In
December 2020
205 pages
ISBN:9781450387507
DOI:10.1145/3430036
- General Chairs:
- Michael Burch,
- Michel Westenberg,
- Program Chairs:
- Quang Vinh Nguyen,
- Ying Zhao
Copyright © 2020 Owner/Author.
Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 08 December 2020
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VINCI 2020
VINCI 2020: The 13th International Symposium on Visual Information Communication and Interaction
December 8 - 10, 2020
Eindhoven, Netherlands
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Overall Acceptance Rate 71 of 193 submissions, 37%
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