research-article

Community-Based Network Alignment for Large Attributed Network

Authors:

Wei-Shih YangAuthors Info & Claims

CIKM '17: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management

Pages 587 - 596

https://doi.org/10.1145/3132847.3132904

Published: 06 November 2017 Publication History

Abstract

Network alignment is becoming an active topic in network data analysis. Despite extensive research, we realize that efficient use of topological and attribute information for large attributed network alignment has not been sufficiently addressed in previous studies. In this paper, based on Stochastic Block Model (SBM) and Dirichlet-multinomial, we propose "divide-and-conquer" models CAlign that jointly consider network alignment, community discovery and community alignment in one framework for large networks with node attributes, in an effort to reduce both the computation time and memory usage while achieving better or competitive performance. It is provable that the algorithms derived from our model have sub-quadratic time complexity and linear space complexity on a network with small densification power, which is true for most real-world networks. Experiments show CAlign is superior to two recent state-of-art models in terms of accuracy, time and memory on large networks, and CAlign is capable of handling millions of nodes on a modern desktop machine.

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Bhowmick SBell PTaufer M(2023)A Survey of Graph Comparison Methods with Applications to Nondeterminism in High-Performance ComputingThe International Journal of High Performance Computing Applications10.1177/1094342023116661037:3-4(306-327)Online publication date: 5-Apr-2023
https://doi.org/10.1177/10943420231166610
Jiang M(2023)Transfer Learning across Graph Convolutional Networks: Methods, Theory, and ApplicationsACM Transactions on Knowledge Discovery from Data10.1145/361737618:1(1-23)Online publication date: 16-Oct-2023
https://dl.acm.org/doi/10.1145/3617376
Sun LDu YGao SYe JWang FRen FLiang MWang YWang S(2023)GroupAligner: A Deep Reinforcement Learning with Domain Adaptation for Social Group AlignmentACM Transactions on the Web10.1145/358050917:3(1-30)Online publication date: 22-May-2023
https://dl.acm.org/doi/10.1145/3580509
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Index Terms

Community-Based Network Alignment for Large Attributed Network

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Published In

cover image ACM Conferences

CIKM '17: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management

November 2017

2604 pages

ISBN:9781450349185

DOI:10.1145/3132847

General Chairs:
Ee-Peng Lim
Singapore Management University, Singapore
,
Marianne Winslett
University of Illinois at Urbana-Champaign, USA, and Advanced Digital Sciences Center, Singapore
,
Program Chairs:
Mark Sanderson
RMIT, Australia
,
Ada Fu
Chinese University of Hong Kong, Hong Kong
,
Jimeng Sun
Georgia Tech, USA
,
Shane Culpepper
RMIT, Australia
,
Eric Lo
Chinese University of Hong Kong, Hong Kong
,
Joyce Ho
Emory University, USA
,
Debora Donato
Mix Tech, Inc., USA
,
Rakesh Agrawal
Data Insights Laboratories, USA
,
Yu Zheng
Microsoft Research Asia, China
,
Carlos Castillo
Qatar Computing Research Institute, Qatar
,
Aixin Sun
Nanyang Technological University, Singapore
,
Vincent S. Tseng
National Cheng Kung University, Taiwan
,
Chenliang Li
Wuhan University, China

Copyright © 2017 ACM.

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Published: 06 November 2017

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November 6 - 10, 2017

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CIKM '17 Paper Acceptance Rate 171 of 855 submissions, 20%;

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Cited By

Bhowmick SBell PTaufer M(2023)A Survey of Graph Comparison Methods with Applications to Nondeterminism in High-Performance ComputingThe International Journal of High Performance Computing Applications10.1177/1094342023116661037:3-4(306-327)Online publication date: 5-Apr-2023
https://doi.org/10.1177/10943420231166610
Jiang M(2023)Transfer Learning across Graph Convolutional Networks: Methods, Theory, and ApplicationsACM Transactions on Knowledge Discovery from Data10.1145/361737618:1(1-23)Online publication date: 16-Oct-2023
https://dl.acm.org/doi/10.1145/3617376
Sun LDu YGao SYe JWang FRen FLiang MWang YWang S(2023)GroupAligner: A Deep Reinforcement Learning with Domain Adaptation for Social Group AlignmentACM Transactions on the Web10.1145/358050917:3(1-30)Online publication date: 22-May-2023
https://dl.acm.org/doi/10.1145/3580509
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
Jiang MZhu FChin Ooi BMiao CWang HSkrypnyk IHsu WChawla S(2021)Cross-Network Learning with Partially Aligned Graph Convolutional NetworksProceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining10.1145/3447548.3467282(746-755)Online publication date: 14-Aug-2021
https://dl.acm.org/doi/10.1145/3447548.3467282
Feng SShen DNie TKou YYu G(2021)A generation probability based percolation network alignment methodWorld Wide Web10.1007/s11280-021-00893-4Online publication date: 17-Jul-2021
https://doi.org/10.1007/s11280-021-00893-4
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
Zhang STong HTang JXu JFan W(2020)Incomplete Network AlignmentACM Transactions on Knowledge Discovery from Data10.1145/338420314:4(1-26)Online publication date: 30-May-2020
https://dl.acm.org/doi/10.1145/3384203
Li PHuang LWang CLai JHuang D(2020)Community Detection by Motif-Aware Label PropagationACM Transactions on Knowledge Discovery from Data10.1145/337853714:2(1-19)Online publication date: 9-Feb-2020
https://dl.acm.org/doi/10.1145/3378537
Sun LZhang ZZhang JWang FDu YSu SYu P(2020)PERFECT: A Hyperbolic Embedding for Joint User and Community Alignment2020 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM50108.2020.00059(501-510)Online publication date: Nov-2020
https://doi.org/10.1109/ICDM50108.2020.00059
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