research-article

Distributed Triangle Approximately Counting Algorithms in Simple Graph Stream

Authors:

Ming LiuAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 16, Issue 4

Article No.: 79, Pages 1 - 43

https://doi.org/10.1145/3494562

Published: 08 January 2022 Publication History

Abstract

Recently, the counting algorithm of local topology structures, such as triangles, has been widely used in social network analysis, recommendation systems, user portraits and other fields. At present, the problem of counting global and local triangles in a graph stream has been widely studied, and numerous triangle counting steaming algorithms have emerged. To improve the throughput and scalability of streaming algorithms, many researches of distributed streaming algorithms on multiple machines are studied. In this article, we first propose a framework of distributed streaming algorithm based on the Master-Worker-Aggregator architecture. The two core parts of this framework are an edge distribution strategy, which plays a key role to affect the performance, including the communication overhead and workload balance, and aggregation method, which is critical to obtain the unbiased estimations of the global and local triangle counts in a graph stream. Then, we extend the state-of-the-art centralized algorithm TRIÈST into four distributed algorithms under our framework. Compared to their competitors, experimental results show that DVHT-i is excellent in accuracy and speed, performing better than the best existing distributed streaming algorithm. DEHT-b is the fastest algorithm and has the least communication overhead. What’s more, it almost achieves absolute workload balance.

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Cited By

Gu JSong CDai HLu LLiu M(2024)Compact Estimator for Streaming Triangle CountingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.337122836:8(3712-3724)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1109/TKDE.2024.3371228
Xuan WLiang YCao HLin NYe XFan D(2024)DTC: Real-Time and Accurate Distributed Triangle Counting in Fully Dynamic Graph Streams2024 43rd International Symposium on Reliable Distributed Systems (SRDS)10.1109/SRDS64841.2024.00028(198-209)Online publication date: 30-Sep-2024
https://doi.org/10.1109/SRDS64841.2024.00028
Yang XSong CGu JLi KLi H(2023)A distributed streaming framework for edge–cloud triangle counting in graph streamsKnowledge-Based Systems10.1016/j.knosys.2023.110878278:COnline publication date: 25-Oct-2023
https://dl.acm.org/doi/10.1016/j.knosys.2023.110878
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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 16, Issue 4

August 2022

529 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3505210

Editor:
Charu Aggarwal
IBM T. J. Watson Research, USA

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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

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Publication History

Published: 08 January 2022

Accepted: 01 October 2021

Revised: 01 September 2021

Received: 01 May 2021

Published in TKDD Volume 16, Issue 4

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National Natural Science Foundation of China
Sichuan Science and Technology Program
The Science and Technology Achievements Transformation Demonstration Project of Sichuan Province of China
The Fundamental Research Funds for the Central Universities

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Cited By

Gu JSong CDai HLu LLiu M(2024)Compact Estimator for Streaming Triangle CountingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.337122836:8(3712-3724)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1109/TKDE.2024.3371228
Xuan WLiang YCao HLin NYe XFan D(2024)DTC: Real-Time and Accurate Distributed Triangle Counting in Fully Dynamic Graph Streams2024 43rd International Symposium on Reliable Distributed Systems (SRDS)10.1109/SRDS64841.2024.00028(198-209)Online publication date: 30-Sep-2024
https://doi.org/10.1109/SRDS64841.2024.00028
Yang XSong CGu JLi KLi H(2023)A distributed streaming framework for edge–cloud triangle counting in graph streamsKnowledge-Based Systems10.1016/j.knosys.2023.110878278:COnline publication date: 25-Oct-2023
https://dl.acm.org/doi/10.1016/j.knosys.2023.110878
Yu CLiu HWahab FLing ZRen TMa HZhao Y(2023)Global triangle estimation based on first edge sampling in large graph streamsThe Journal of Supercomputing10.1007/s11227-023-05205-379:13(14079-14116)Online publication date: 3-Apr-2023
https://dl.acm.org/doi/10.1007/s11227-023-05205-3
Taniguchi RAmagata DHara T(2022)Efficient Retrieval of Top-k Weighted Triangles on Static and Dynamic Spatial DataIEEE Access10.1109/ACCESS.2022.317762010(55298-55307)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3177620

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