short-paper

A Cohesive Structure Based Bipartite Graph Analytics System

Authors:

Ying ZhangAuthors Info & Claims

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

Pages 4799 - 4803

https://doi.org/10.1145/3459637.3481963

Published: 30 October 2021 Publication History

Abstract

Bipartite graphs arise naturally when modeling two different types of entities such as user-item, author-paper, and director-board. In recent years, driven by numerous real-world applications in these networks, mining cohesive structures in bipartite graphs becomes a popular research topic. In this paper, we propose the first cohesive-structure-based bipartite graph analytics system, CohBGA. The key innovative features of our system are as follows. Firstly, we involve several cohesive-structure-based models and statistics in our system to analyze bipartite graphs at different levels of granularity. Secondly, CohBGA has a user-friendly and interactive visual interface with various functional tools to meet users' diverse query requirements. Thirdly, we implement state-of-the-art algorithms in CohBGA to support efficient query processing. Furthermore, as a generic framework is designed in CohBGA, CohBGA is going to be an open-source bipartite graph analytics platform that allows researchers to evaluate the effectiveness of more cohesive-structure-based models and algorithms for bipartite graphs.

Supplementary Material

MP4 File (New Recording 6_1.mp4)

This is a presentation video for our Cohesive Structure Based Bipartite Graph Analytics System, CohBGA. CohBGA has a user-friendly and interactive visual interface with various functional tools. It can be utilized to analyze bipartite graphs based on cohesive structures including butterfly, (alpha, beta)-core, and bitruss.

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Cited By

Wang ZLai LLiu YShui BTian CZhong S(2024)Parallelization of butterfly counting on hierarchical memoryThe VLDB Journal10.1007/s00778-024-00856-x33:5(1453-1484)Online publication date: 7-Jun-2024
https://doi.org/10.1007/s00778-024-00856-x
Zhang FChen DWang SYang YGan J(2023)Scalable Approximate Butterfly and Bi-triangle Counting for Large Bipartite NetworksProceedings of the ACM on Management of Data10.1145/36267531:4(1-26)Online publication date: 12-Dec-2023
https://doi.org/10.1145/3626753
Luo WYang QFang YZhou X(2023)Efficient Core Maintenance in Large Bipartite GraphsProceedings of the ACM on Management of Data10.1145/36173291:3(1-26)Online publication date: 13-Nov-2023
https://dl.acm.org/doi/10.1145/3617329
Show More Cited By

Index Terms

A Cohesive Structure Based Bipartite Graph Analytics System

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cover image ACM Conferences

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

October 2021

4966 pages

ISBN:9781450384469

DOI:10.1145/3459637

General Chairs:
Gianluca Demartini
The University of Queensland, Australia
,
Guido Zuccon
The University of Queensland, Australia
,
Program Chairs:
J. Shane Culpepper
RMIT University, Australia
,
Zi Huang
The University of Queensland, Australia
,
Hanghang Tong
University of Illinois at Urbana-Champaign, USA

Copyright © 2021 ACM.

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Published: 30 October 2021

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November 1 - 5, 2021

Queensland, Virtual Event, Australia

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Cited By

Wang ZLai LLiu YShui BTian CZhong S(2024)Parallelization of butterfly counting on hierarchical memoryThe VLDB Journal10.1007/s00778-024-00856-x33:5(1453-1484)Online publication date: 7-Jun-2024
https://doi.org/10.1007/s00778-024-00856-x
Zhang FChen DWang SYang YGan J(2023)Scalable Approximate Butterfly and Bi-triangle Counting for Large Bipartite NetworksProceedings of the ACM on Management of Data10.1145/36267531:4(1-26)Online publication date: 12-Dec-2023
https://doi.org/10.1145/3626753
Luo WYang QFang YZhou X(2023)Efficient Core Maintenance in Large Bipartite GraphsProceedings of the ACM on Management of Data10.1145/36173291:3(1-26)Online publication date: 13-Nov-2023
https://dl.acm.org/doi/10.1145/3617329
Wang ZLai LLiu YShui BTian CZhong S(2023)I/O-Efficient Butterfly Counting at ScaleProceedings of the ACM on Management of Data10.1145/35887141:1(1-27)Online publication date: 30-May-2023
https://dl.acm.org/doi/10.1145/3588714
Wang KZhao GZhang WLin XZhang YHe YLi C(2023)Cohesive Subgraph Discovery Over Uncertain Bipartite GraphsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.323456735:11(11165-11179)Online publication date: 6-Jan-2023
https://dl.acm.org/doi/10.1109/TKDE.2023.3234567
Zhao GWang KZhang WLin XZhang YHe Y(2022)Efficient Computation of Cohesive Subgraphs in Uncertain Bipartite Graphs2022 IEEE 38th International Conference on Data Engineering (ICDE)10.1109/ICDE53745.2022.00220(2333-2345)Online publication date: May-2022
https://doi.org/10.1109/ICDE53745.2022.00220
Miao XLiu YChen LGao YYin J(2022)Reliable Community Search on Uncertain Graphs2022 IEEE 38th International Conference on Data Engineering (ICDE)10.1109/ICDE53745.2022.00092(1166-1179)Online publication date: May-2022
https://doi.org/10.1109/ICDE53745.2022.00092

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