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.

References

[1]

Zhang, J. and Yu, P. S. Pct: partial co-alignment of social networks. World Wide Web Conference, 2016.

Digital Library

[2]

Zhang, Y., Tang, J., Yang, Z., Pei, J. and Yu, P. S. Cosnet: Connecting heterogeneous social networks with local and global consistency. 2015.

Digital Library

[3]

Liu, R., Cheng, W., Tong, H., Wang, W. and Zhang, X. Robust Multi-Network Clustering via Joint Cross-Domain Cluster Alignment. 2015.

Digital Library

[4]

Singh, R., Xu, J. and Berger, B. Global alignment of multiple protein interaction networks with application to functional orthology detection. Proceedings of the National Academy of Sciences, 105, 35 (2008), 12763--12768.

[5]

Crawford, J., Sun, Y. and Milenkovi?, T. Fair evaluation of global network aligners. Algorithms for Molecular Biology, 10, 1 (2015), 19.

[6]

Song, B., Gao, J., Ke, W. and Hu, X. Achieving high k-coverage and k-consistency in global alignment of multiple PPI networks. 2016.

[7]

Lacoste-Julien, S., Palla, K., Davies, A., Kasneci, G., Graepel, T. and Ghahramani, Z. Sigma: Simple greedy matching for aligning large knowledge bases. 2013.

[8]

Smalter, A., Huan, J. and Lushington, G. Gpm: A graph pattern matching kernel with diffusion for chemical compound classification. 2008.

[9]

Singh, R., Xu, J. and Berger, B. Pairwise global alignment of protein interaction networks by matching neighborhood topology. 2007.

[10]

Bayati, M., Gerritsen, M., Gleich, D. F., Saberi, A. and Wang, Y. Algorithms for large, sparse network alignment problems. 2009.

Digital Library

[11]

Koutra, D., Tong, H. and Lubensky, D. Big-align: Fast bipartite graph alignment. 2013.

[12]

Zhang, S. and Tong, H. Final: Fast attributed network alignment. 2016.

[13]

Yang, J. and Leskovec, J. Defining and evaluating network communities based on ground-truth. Knowledge and Information Systems, 42, 1 (2015), 181--213.

Digital Library

[14]

Aggarwal, C. C. An introduction to social network data analytics. Social network data analytics (2011), 1--15.

[15]

Holland, P. W., Laskey, K. B. and Leinhardt, S. Stochastic blockmodels: First steps. Social networks, 5, 2 (1983), 109--137.

[16]

Abbe, E. Community detection and the stochastic block model (2016).

[17]

Karrer, B. and Newman, M. E. Stochastic blockmodels and community structure in networks. Physical Review E, 83, 1 (2011), 016107.

[18]

Frieze, A. and Karo'ski, M. Introduction to random graphs. Cambridge University Press, 2015.

[19]

Durrett, R. Random graph dynamics. Cambridge university press Cambridge, 2007.

Digital Library

[20]

Leskovec, J., Kleinberg, J. and Faloutsos, C. Graphs over time: densification laws, shrinking diameters and possible explanations. 2005.

Digital Library

[21]

Ng, K. W., Tian, G.-L. and Tang, M.-L. Dirichlet and related distributions: Theory, methods and applications. John Wiley & Sons, 2011.

[22]

Frigyik, B. A., Kapila, A. and Gupta, M. R. Introduction to the Dirichlet distribution and related processes. Department of Electrical Engineering, University of Washignton, UWEETR-2010-0006 (2010).

[23]

Weston, J., Elisseeff, A., Schölkopf, B. and Tipping, M. Use of the zero-norm with linear models and kernel methods. Journal of machine learning research, 3, Mar (2003), 1439--1461.

Digital Library

[24]

Aicher, C., Jacobs, A. Z. and Clauset, A. Adapting the stochastic block model to edge-weighted networks. arXiv preprint arXiv:1305.5782 (2013).

[25]

Larremore, D. B., Clauset, A. and Jacobs, A. Z. Efficiently inferring community structure in bipartite networks. Physical Review E, 90, 1 (2014), 012805.

[26]

Barabási, A.-L. Network science. Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 371, 1987 (2013), 20120375.

[27]

Minka, T. Estimating a Dirichlet distribution. Technical report, 2000.

[28]

Stewart, G. W. Perturbation theory for the singular value decomposition. 1998.

[29]

Yang, J., McAuley, J. and Leskovec, J. Community detection in networks with node attributes. 2013.

[30]

Airoldi, E. M., Blei, D. M., Fienberg, S. E. and Xing, E. P. Mixed Membership Stochastic Blockmodels. Journal of Machine Learning Research, 9 (2008), 1981--2014.

Digital Library

[31]

Leskovec, J., Adamic, L. A. and Huberman, B. A. The dynamics of viral marketing. ACM Transactions on the Web (TWEB), 1, 1 (2007), 5.

Digital Library

[32]

Zheng Chen, E. Y. Domain-Independent Term Extraction & Term Network for Scientific Publications. In Proceedings of the iConference 2017 (2017).

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
Show More Cited By

Index Terms

Community-Based Network Alignment for Large Attributed Network

Recommendations

FINAL: Fast Attributed Network Alignment
KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Multiple networks naturally appear in numerous high-impact applications. Network alignment (i.e., finding the node correspondence across different networks) is often the very first step for many data mining tasks. Most, if not all, of the existing ...
Attributed network community detection based on network embedding and parameter-free clustering
Abstract
In recent years, many attributednetwork have emerged, such as Facebook networks in social networks, protein networks and academic citation networks. In order to find communities where the nodes are tightly connected and have attributes similar to ...
Cross-Network Embedding for Multi-Network Alignment
WWW '19: The World Wide Web Conference

Recently, data mining through analyzing the complex structure and diverse relationships on multi-network has attracted much attention in both academia and industry. One crucial prerequisite for this kind of multi-network mining is to map the nodes ...

Comments

Information & Contributors

Information

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.

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 ACM 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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 November 2017

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

CIKM '17

Sponsor:

CIKM '17: ACM Conference on Information and Knowledge Management

November 6 - 10, 2017

Singapore, Singapore

Acceptance Rates

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

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

Upcoming Conference

CIKM '25

Sponsor:
sigir
sigir

The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

Seoul , Republic of Korea

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

16
Total Citations
View Citations
396
Total Downloads

Downloads (Last 12 months)16
Downloads (Last 6 weeks)5

Reflects downloads up to 01 Mar 2025

Other Metrics

View Author Metrics

Citations

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
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten