short-paper

OPTANE: an OPtimal transport algorithm for NEtwork alignment

Authors:

Farzan Masrour,

Abdol-Hossein EsfahanianAuthors Info & Claims

ASONAM '19: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

Pages 448 - 451

https://doi.org/10.1145/3341161.3342937

Published: 15 January 2020 Publication History

Abstract

Networks provide a powerful representation tool for modeling dyadic interactions among interconnected entities in a complex system. For many applications such as social network analysis, it is common for the entities to appear in more than one network. Network alignment (NA) is an important first step towards learning the entities' behavior across multiple networks by finding the correspondence between similar nodes in different networks. However, learning the proper alignment matrix in noisy networks is a challenge due to the difficulty in preserving both the neighborhood topology and feature consistency of the aligned nodes. In this paper, we present OPTANE, a robust unsupervised network alignment framework, inspired from an optimal transport theory perspective. The framework provides a principled way to combine node similarity with topology information to learn the alignment matrix. Experimental results conducted on both synthetic and real-world data attest to the effectiveness of the OPTANE framework compared to other baseline approaches.

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Cited By

Yang LWang XZhang JYang JXu YHou JXin KWang F(2023)HackGAN: Harmonious Cross-Network Mapping Using CycleGAN With Wasserstein–Procrustes Learning for Unsupervised Network AlignmentIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.314435010:2(746-759)Online publication date: Apr-2023
https://doi.org/10.1109/TCSS.2022.3144350
Zhou YRen JJin RZhang ZZheng JJiang ZYan DDou D(2021)Unsupervised Adversarial Network Alignment with Reinforcement LearningACM Transactions on Knowledge Discovery from Data10.1145/347705016:3(1-29)Online publication date: 22-Oct-2021
https://dl.acm.org/doi/10.1145/3477050
Zhang ZZhang ZZhou YShen YJin RDou DLarochelle HRanzato MHadsell RBalcan MLin H(2020)Adversarial attacks on deep graph matchingProceedings of the 34th International Conference on Neural Information Processing Systems10.5555/3495724.3497474(20834-20851)Online publication date: 6-Dec-2020
https://dl.acm.org/doi/10.5555/3495724.3497474

OPTANE: an OPtimal transport algorithm for NEtwork alignment
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms
2. Information systems
  1. Information systems applications

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Published In

cover image ACM Conferences

ASONAM '19: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

August 2019

1228 pages

ISBN:9781450368681

DOI:10.1145/3341161

Editors:
Francesca Spezzano
Boise State University
,
Wei Chen
Microsoft Research, China
,
Xiaokui Xiao
National University of Singapore, Singapore

Copyright © 2019 ACM.

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 the author(s) 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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Published: 15 January 2020

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ASONAM '19

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SIGKDD

ASONAM '19: International Conference on Advances in Social Networks Analysis and Mining

August 27 - 30, 2019

British Columbia, Vancouver, Canada

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ASONAM '19 Paper Acceptance Rate 41 of 286 submissions, 14%;

Overall Acceptance Rate 116 of 549 submissions, 21%

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Cited By

Yang LWang XZhang JYang JXu YHou JXin KWang F(2023)HackGAN: Harmonious Cross-Network Mapping Using CycleGAN With Wasserstein–Procrustes Learning for Unsupervised Network AlignmentIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.314435010:2(746-759)Online publication date: Apr-2023
https://doi.org/10.1109/TCSS.2022.3144350
Zhou YRen JJin RZhang ZZheng JJiang ZYan DDou D(2021)Unsupervised Adversarial Network Alignment with Reinforcement LearningACM Transactions on Knowledge Discovery from Data10.1145/347705016:3(1-29)Online publication date: 22-Oct-2021
https://dl.acm.org/doi/10.1145/3477050
Zhang ZZhang ZZhou YShen YJin RDou DLarochelle HRanzato MHadsell RBalcan MLin H(2020)Adversarial attacks on deep graph matchingProceedings of the 34th International Conference on Neural Information Processing Systems10.5555/3495724.3497474(20834-20851)Online publication date: 6-Dec-2020
https://dl.acm.org/doi/10.5555/3495724.3497474

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