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Incomplete Network Alignment: Problem Definitions and Fast Solutions

Authors:

Wei FanAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 14, Issue 4

Article No.: 38, Pages 1 - 26

https://doi.org/10.1145/3384203

Published: 30 May 2020 Publication History

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Abstract

Networks are prevalent in many areas and are often collected from multiple sources. However, due to the veracity characteristics, more often than not, networks are incomplete. Network alignment and network completion have become two fundamental cornerstones behind a wealth of high-impact graph mining applications. The state-of-the-art have been addressing these two tasks in parallel. That is, most of the existing network alignment methods have implicitly assumed that the topology of the input networks for alignment are perfectly known a priori, whereas the existing network completion methods admit either a single network (i.e., matrix completion) or multiple aligned networks (e.g., tensor completion). In this article, we argue that network alignment and completion are inherently complementary with each other, and hence propose to jointly address them so that the two tasks can mutually benefit from each other. We formulate the problem from the optimization perspective, and propose an effective algorithm (iNeAt) to solve it. The proposed method offers two distinctive advantages. First (Alignment accuracy), our method benefits from the higher-quality input networks while mitigates the effect of the incorrectly inferred links introduced by the completion task itself. Second (Alignment efficiency), thanks to the low-rank structure of the complete networks and the alignment matrix, the alignment process can be significantly accelerated. We perform extensive experiments which show that (1) the network completion can significantly improve the alignment accuracy, i.e., up to 30% over the baseline methods; (2) the network alignment can in turn help recover more missing edges than the baseline methods; and (3) our method achieves a good balance between the running time and the accuracy, and scales with a provable linear complexity in both time and space.

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Cited By

Liu LChen PLi XCheung WZhang YLiu QWang G(2024)WL-Align: Weisfeiler-Lehman Relabeling for Aligning Users Across Networks via Regularized Representation LearningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.327784336:1(445-458)Online publication date: Jan-2024
https://doi.org/10.1109/TKDE.2023.3277843
Li ZFu DHe J(2023)Everything Evolves in Personalized PageRankProceedings of the ACM Web Conference 202310.1145/3543507.3583474(3342-3352)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543507.3583474
Zeng ZZhang SXia YTong H(2023)PARROT: Position-Aware Regularized Optimal Transport for Network AlignmentProceedings of the ACM Web Conference 202310.1145/3543507.3583357(372-382)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543507.3583357
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Index Terms

Incomplete Network Alignment: Problem Definitions and Fast Solutions
1. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 14, Issue 4

August 2020

316 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3403605

Editors:
Charu Aggarwal
IBM T. J. Watson Research, USA
,
Xindong Wu
Minginglamp Academy of Sciences, China

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Copyright © 2020 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 May 2020

Online AM: 07 May 2020

Accepted: 01 February 2020

Revised: 01 November 2019

Received: 01 May 2018

Published in TKDD Volume 14, Issue 4

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Cited By

Liu LChen PLi XCheung WZhang YLiu QWang G(2024)WL-Align: Weisfeiler-Lehman Relabeling for Aligning Users Across Networks via Regularized Representation LearningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.327784336:1(445-458)Online publication date: Jan-2024
https://doi.org/10.1109/TKDE.2023.3277843
Li ZFu DHe J(2023)Everything Evolves in Personalized PageRankProceedings of the ACM Web Conference 202310.1145/3543507.3583474(3342-3352)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543507.3583474
Zeng ZZhang SXia YTong H(2023)PARROT: Position-Aware Regularized Optimal Transport for Network AlignmentProceedings of the ACM Web Conference 202310.1145/3543507.3583357(372-382)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543507.3583357
Tang WSun HWang JLiu CQi QWang JLiao J(2023)Identifying Users Across Social Media Networks for Interpretable Fine-Grained Neighborhood Matching by Adaptive GATIEEE Transactions on Services Computing10.1109/TSC.2023.328887216:5(3453-3466)Online publication date: Sep-2023
https://doi.org/10.1109/TSC.2023.3288872
Zhang YZhang YLi YLi L(2023)Aligning Users across Social Networks via Integrating Structural Similarity and Graph Representation Learning2023 9th International Conference on Big Data and Information Analytics (BigDIA)10.1109/BigDIA60676.2023.10429339(549-556)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigDIA60676.2023.10429339
Wang YWang WZhen ZPeng QJiao PLiang WShao MSun Y(2022)Geometry interaction network alignmentNeurocomputing10.1016/j.neucom.2022.06.077501(618-628)Online publication date: Aug-2022
https://doi.org/10.1016/j.neucom.2022.06.077
Roach SNi CKopylov ALu TXu JZhang SDu BZhou DWu JLiu LYan YHe JTong H(2020)CANON: Complex Analytics of Network of Networks for Modeling Adversarial Activities2020 IEEE International Conference on Big Data (Big Data)10.1109/BigData50022.2020.9378258(1634-1643)Online publication date: 10-Dec-2020
https://doi.org/10.1109/BigData50022.2020.9378258

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