research-article

Time-topology analysis on temporal graphs

Authors:

Jeffrey Xu YuAuthors Info & Claims

The VLDB Journal, Volume 32, Issue 4

Pages 815 - 843

https://doi.org/10.1007/s00778-022-00772-y

Published: 06 January 2023 Publication History

Abstract

Many real-world networks have been evolving and are finely modeled as temporal graphs from the viewpoint of the graph theory. A temporal graph is informative and always contains two types of features, i.e., the temporal feature and topological feature, where the temporal feature is related to the establishing time of the relationships in the temporal graph, and the topological feature is influenced by the structure of the graph. In this paper, considering both these two types of features, we perform time-topology analysis on temporal graphs to analyze the cohesiveness of temporal graphs and extract cohesive subgraphs. Firstly, a new metric named

T

-cohesiveness is proposed to evaluate the cohesiveness of a temporal subgraph from the time and topology dimensions jointly. Specifically, given a temporal graph

G_{s} = (V_{s}, E_{s})

, cohesiveness in the time dimension reflects whether the connections in

G_{s}

happen in a short period of time, while cohesiveness in the topology dimension indicates whether the vertices in

V_{s}

are densely connected and have few connections with vertices out of

G_{s}

. Then,

T

-cohesiveness is utilized to perform time-topology analysis on temporal graphs, and two time-topology analysis methods are proposed. In detail,

T

-cohesiveness evolution tracking traces the evolution of the

T

-cohesiveness of a subgraph, and combo searching finds out cohesive subgraphs containing the query vertex, which have

T

-cohesiveness values larger than a given threshold. Moreover, since combo searching is NP-hard, a pruning strategy is proposed to estimate the upper bound of the

T

-cohesiveness value, and then improve the efficiency of combo searching. Experimental results demonstrate the efficiency of the proposed time-topology analysis methods and the pruning strategy. Besides, four more definitions of

T

-cohesiveness are compared with our method. The experimental results confirm the superiority of our definition.

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Cited By

Jiang JChen YHe BChen MChen J(2024)Spade+: A Generic Real-Time Fraud Detection Framework on Dynamic GraphsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.339415536:11(7058-7073)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1109/TKDE.2024.3394155
Li FZou ZLiu XLi JYang XWang B(2023)Detecting maximum k-durable structures on temporal graphsKnowledge-Based Systems10.1016/j.knosys.2023.110561271:COnline publication date: 8-Jul-2023
https://dl.acm.org/doi/10.1016/j.knosys.2023.110561

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Published In

cover image The VLDB Journal — The International Journal on Very Large Data Bases

The VLDB Journal — The International Journal on Very Large Data Bases Volume 32, Issue 4

Jul 2023

243 pages

ISSN:1066-8888

Issue’s Table of Contents

© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 06 January 2023

Accepted: 02 December 2022

Revision received: 07 July 2022

Received: 23 February 2022

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Cited By

Jiang JChen YHe BChen MChen J(2024)Spade+: A Generic Real-Time Fraud Detection Framework on Dynamic GraphsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.339415536:11(7058-7073)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1109/TKDE.2024.3394155
Li FZou ZLiu XLi JYang XWang B(2023)Detecting maximum k-durable structures on temporal graphsKnowledge-Based Systems10.1016/j.knosys.2023.110561271:COnline publication date: 8-Jul-2023
https://dl.acm.org/doi/10.1016/j.knosys.2023.110561

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