research-article

Multi-relation Graph Summarization

Authors:

Francesco BonchiAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 16, Issue 5

Article No.: 82, Pages 1 - 30

https://doi.org/10.1145/3494561

Published: 09 March 2022 Publication History

Abstract

Graph summarization is beneficial in a wide range of applications, such as visualization, interactive and exploratory analysis, approximate query processing, reducing the on-disk storage footprint, and graph processing in modern hardware. However, the bulk of the literature on graph summarization surprisingly overlooks the possibility of having edges of different types. In this article, we study the novel problem of producing summaries of multi-relation networks, i.e., graphs where multiple edges of different types may exist between any pair of nodes. Multi-relation graphs are an expressive model of real-world activities, in which a relation can be a topic in social networks, an interaction type in genetic networks, or a snapshot in temporal graphs.

The first approach that we consider for multi-relation graph summarization is a two-step method based on summarizing each relation in isolation, and then aggregating the resulting summaries in some clever way to produce a final unique summary. In doing this, as a side contribution, we provide the first polynomial-time approximation algorithm based on the k-Median clustering for the classic problem of lossless single-relation graph summarization.

Then, we demonstrate the shortcomings of these two-step methods, and propose holistic approaches, both approximate and heuristic algorithms, to compute a summary directly for multi-relation graphs. In particular, we prove that the approximation bound of k-Median clustering for the single relation solution can be maintained in a multi-relation graph with proper aggregation operation over adjacency matrices corresponding to its multiple relations. Experimental results and case studies (on co-authorship networks and brain networks) validate the effectiveness and efficiency of the proposed algorithms.

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Cited By

Chu DZhang FZhang WZhang YLin X(2024)Graph Summarization: Compactness Meets EfficiencyProceedings of the ACM on Management of Data10.1145/36549432:3(1-26)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3654943
Zhong HLoukides GConte APissis S(2024)Ego-Network Segmentation via (Weighted) Jaccard MedianIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.337371236:9(4646-4663)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1109/TKDE.2024.3373712
Supriyono Wibawa ASuyono Kurniawan F(2024)A survey of text summarization: Techniques, evaluation and challengesNatural Language Processing Journal10.1016/j.nlp.2024.1000707(100070)Online publication date: Jun-2024
https://doi.org/10.1016/j.nlp.2024.100070
Show More Cited By

Index Terms

Multi-relation Graph Summarization
1. Information systems
  1. Data management systems
    1. Database design and models
      1. Graph-based database models
        Network data models
  2. Information systems applications
    1. Data mining

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cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 16, Issue 5

October 2022

532 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3514187

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

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Publication History

Published: 09 March 2022

Accepted: 01 October 2021

Revised: 01 July 2021

Received: 01 April 2021

Published in TKDD Volume 16, Issue 5

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https://dl.acm.org/doi/10.1145/3654943
Zhong HLoukides GConte APissis S(2024)Ego-Network Segmentation via (Weighted) Jaccard MedianIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.337371236:9(4646-4663)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1109/TKDE.2024.3373712
Supriyono Wibawa ASuyono Kurniawan F(2024)A survey of text summarization: Techniques, evaluation and challengesNatural Language Processing Journal10.1016/j.nlp.2024.1000707(100070)Online publication date: Jun-2024
https://doi.org/10.1016/j.nlp.2024.100070
Liu QWei JLiu HJi Y(2023)A Hierarchical Parallel Graph Summarization Approach Based on Ranking NodesApplied Sciences10.3390/app1308466413:8(4664)Online publication date: 7-Apr-2023
https://doi.org/10.3390/app13084664
Liang YLiu LHuangfu LSamtani SYu ZZeng D(2023)Learning Entangled Interactions of Complex Causality via Self-Paced Contrastive LearningACM Transactions on Knowledge Discovery from Data10.1145/363240618:3(1-24)Online publication date: 9-Dec-2023
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Wang GZeng YLi RQin HShi XXia YShang XHong L(2023)Temporal Graph CubeIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.327046035:12(13015-13030)Online publication date: 26-Apr-2023
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