research-article

Understanding task-driven information flow in collaborative networks

Authors:

Louise E. Moser,

Xifeng YanAuthors Info & Claims

WWW '12: Proceedings of the 21st international conference on World Wide Web

Pages 849 - 858

https://doi.org/10.1145/2187836.2187951

Published: 16 April 2012 Publication History

Abstract

Collaborative networks are a special type of social network formed by members who collectively achieve specific goals, such as fixing software bugs and resolving customers' problems. In such networks, information flow among members is driven by the tasks assigned to the network, and by the expertise of its members to complete those tasks. In this work, we analyze real-life collaborative networks to understand their common characteristics and how information is routed in these networks. Our study shows that collaborative networks exhibit significantly different properties compared with other complex networks. Collaborative networks have truncated power-law node degree distributions and other organizational constraints. Furthermore, the number of steps along which information is routed follows a truncated power-law distribution. Based on these observations, we developed a network model that can generate synthetic collaborative networks subject to certain structure constraints. Moreover, we developed a routing model that emulates task-driven information routing conducted by human beings in a collaborative network. Together, these two models can be used to study the efficiency of information routing for different types of collaborative networks -- a problem that is important in practice yet difficult to solve without the method proposed in this paper.

References

[1]

Bugzilla: http://www.bugzilla.org/.

[2]

Eclipse: http://www.eclipse.org/.

[3]

Mozilla: http://www.mozilla.org/.

[4]

Msr 2011 challenge: http://2011.msrconf.org/msr-challenge.html.

[5]

A. L. Barabasi and R. Albert. Emergence of scaling in random networks. Science, 286(5439):509--512, 1999.

[6]

M. Boguna, D. Krioukov, and K. C. Claffy. Navigability of complex networks. Nature Physics, 5(1):74--80, 2008.

[7]

A. Clauset, C. R. Shalizi, and M. E. J. Newman. Power-law distributions in empirical data. SIAM Review, 51:661--703, 2009.

Digital Library

[8]

P. Domingos and M. Richardson. Mining the network value of customers. In SIGKDD, pages 57--66, 2001.

Digital Library

[9]

P. Erdös and A. Rényi. On random graphs I. Publicationes Mathematicae, 6:290--297, 1959.

[10]

S. Galam. Minority opinion spreading in random geometry. The European Physical Journal B - Condensed Matter and Complex Systems, 25(4):403--406, 2002.

[11]

S. Galam. Modelling rumors: The no plane Pentagon French hoax case. Physica A: Statistical Mechanics and Its Applications, 320:571--580, 2003.

[12]

X. Guardiola, A. Diaz-Guilera, C. J. Perez, A. Arenas, and M. Llas. Modeling diffusion of innovations in a social network. Phys. Rev. E, 66:026121, 2002.

[13]

D. Kempe, J. Kleinberg, and E. Tardos. Maximizing the spread of influence through a social network. In SIGKDD, pages 137--146, 2003.

Digital Library

[14]

J. Kleinberg. Small-world phenomena and the dynamics of information. In NIPS, page 2001. MIT Press, 2001.

[15]

A. Lloyd and R. May. How viruses spread among computers and people. Science, 292(5520):1316--1317, 2001.

[16]

D. Makowiec. Evolving network - simulation study. The European Physical Journal B - Condensed Matter and Complex Systems, 48:547--555, 2005.

[17]

K. Malarz, Z. Szvetelszky, B. Szekf, and K. Kulakowski. Gossip in random networks. ACTA Physica Polonica B, 37, Nov. 2006.

[18]

G. Miao, L. E. Moser, X. Yan, S. Tao, Y. Chen, and N. Anerousis. Generative models for ticket resolution in expert networks. In KDD, pages 733--742, 2010.

Digital Library

[19]

S. Milgram. The small world problem. Psychology Today, 2:60--67, 1967.

[20]

W. H. Press, B. P. Flannery, S. A. Teukolsky, and W. T. Vetterling. Numerical Recipes in C: The Art of Scientific Computing. Cambridge University Press, 2nd edition, Oct. 1992.

Digital Library

[21]

S. Ree. Power-law distributions from additive preferential redistributions. Phys. Rev. E, 73:026115, Feb. 2006.

[22]

E. M. Rogers. Diffusion of Innovations. Free Press, 4th edition, 1995.

[23]

S. T. Roweis and L. K. Saul. Nonlinear dimensionality reduction by locally linear embedding. Science, 290:2323--2326, 2000.

[24]

A. Sala, L. Cao, C. Wilson, R. Zablit, H. Zheng, and B. Y. Zhao. Measurement-calibrated graph models for social network experiments. In WWW, pages 861--870, 2010.

Digital Library

[25]

R. P. Satorras and A. Vespignani. Epidemic spreading in scale-free networks. Phys. Rev., 86(14):3200--3203, 2001.

[26]

M. Serrano, D. Krioukov, and M. Boguna. Self-similarity of complex networks and hidden metric spaces. Phys. Rev., 078701, 2008.

[27]

D. Strang and S. A. Soule. Diffusion in organizations and social movements: From hybrid corn to poison pills. Annual Review of Sociology, 24(1):265--290, 1998.

[28]

R. Taylor. Constrained switchings in graphs. Research report. University of Melbourne, Department of Mathematics, 1980.

[29]

J. Travers and S. Milgram. An experimental study of the small world problem. Sociometry, 32(4):425--443, 1969.

[30]

T. W. Valente. Network models of the diffusion of innovations. Computational and Mathematical Organization Theory, 2:163--164, 1996.

[31]

D. Wang, Z. Wen, H. Tong, C. Y. Lin, C. Song, and A. L. Barabási. Information spreading in context. In WWW, pages 735--744, 2011.

Digital Library

[32]

D. J. Watts and S. H. Strogatz. Collective dynamics of 'small-world' networks. Nature, 393(6684):440--442, June 1998.

[33]

S. Wu, J. M. Hofman, W. A. Mason, and D. J. Watts. Who says what to whom on Twitter. In WWW, pages 705--714, 2011.

Digital Library

Cited By

Han JSun A(2020)DeepRouting: A Deep Neural Network Approach for Ticket Routing in Expert Network2020 IEEE International Conference on Services Computing (SCC)10.1109/SCC49832.2020.00057(386-393)Online publication date: Nov-2020
https://doi.org/10.1109/SCC49832.2020.00057
Zhang SLu XWang SGuo W(2020)Supply-Demand Matching in Non-Cooperative Social NetworksIEEE Access10.1109/ACCESS.2020.30212868(162458-162466)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3021286
Dimitrov DHelic DStrohmaier M(2018)Tag-Based Navigation and VisualizationSocial Information Access10.1007/978-3-319-90092-6_6(181-212)Online publication date: 3-May-2018
https://doi.org/10.1007/978-3-319-90092-6_6
Show More Cited By

Index Terms

Understanding task-driven information flow in collaborative networks
1. Computing methodologies
  1. Artificial intelligence
    1. Philosophical/theoretical foundations of artificial intelligence
      1. Cognitive science
2. Human-centered computing
  1. Human computer interaction (HCI)

Recommendations

An Analytical Method to Complex Network Behavior Characteristics on the Basis of Information Flow
IMCCC '13: Proceedings of the 2013 Third International Conference on Instrumentation, Measurement, Computer, Communication and Control

A From the perspective of network science, on a basis of complex network, this paper establishes network node information resource model and network information flow model by analyzing the information flow of network. We analyze network behavior ...
Analyzing expert behaviors in collaborative networks
KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining

Collaborative networks are composed of experts who cooperate with each other to complete specific tasks, such as resolving problems reported by customers. A task is posted and subsequently routed in the network from an expert to another until being ...
A novel network optimization partner selection method based on collaborative and knowledge networks
Abstract
Organizational innovation requires strong social collaboration and knowledge networks as well as focused partner selection strategies that complement employee strengths. Therefore, this paper proposes an effective, innovative partner ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

WWW '12: Proceedings of the 21st international conference on World Wide Web

April 2012

1078 pages

ISBN:9781450312295

DOI:10.1145/2187836

General Chairs:
Alain Mille
Université de Lyon, France
,
Fabien Gandon
INRIA, France
,
Jacques Misselis
HP, France
,
Program Chairs:
Michael Rabinovich
Case Western Reserve University, USA
,
Steffen Staab
University of Koblenz-Landau, Germany

Copyright © 2012 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

Univ. de Lyon: Universite de Lyon

In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 April 2012

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

WWW 2012

Sponsor:

Univ. de Lyon

WWW 2012: 21st World Wide Web Conference 2012

April 16 - 20, 2012

Lyon, France

Acceptance Rates

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

10
Total Citations
View Citations
291
Total Downloads

Downloads (Last 12 months)11
Downloads (Last 6 weeks)2

Reflects downloads up to 03 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Han JSun A(2020)DeepRouting: A Deep Neural Network Approach for Ticket Routing in Expert Network2020 IEEE International Conference on Services Computing (SCC)10.1109/SCC49832.2020.00057(386-393)Online publication date: Nov-2020
https://doi.org/10.1109/SCC49832.2020.00057
Zhang SLu XWang SGuo W(2020)Supply-Demand Matching in Non-Cooperative Social NetworksIEEE Access10.1109/ACCESS.2020.30212868(162458-162466)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3021286
Dimitrov DHelic DStrohmaier M(2018)Tag-Based Navigation and VisualizationSocial Information Access10.1007/978-3-319-90092-6_6(181-212)Online publication date: 3-May-2018
https://doi.org/10.1007/978-3-319-90092-6_6
Wang WJiang JAn BJiang YChen B(2017)Toward Efficient Team Formation for Crowdsourcing in Noncooperative Social NetworksIEEE Transactions on Cybernetics10.1109/TCYB.2016.260249847:12(4208-4222)Online publication date: Dec-2017
https://doi.org/10.1109/TCYB.2016.2602498
Han JSun A(2017)Mean Average Distance to Resolver: An Evaluation Metric for Ticket Routing in Expert Network2017 IEEE International Conference on Software Maintenance and Evolution (ICSME)10.1109/ICSME.2017.18(594-602)Online publication date: Sep-2017
https://doi.org/10.1109/ICSME.2017.18
Lamprecht DStrohmaier MHelic DNyulas CTudorache TNoy NMusen M(2015)Using ontologies to model human navigation behavior in information networks: A study based on WikipediaSemantic Web10.3233/SW-1401436:4(403-422)Online publication date: 7-Aug-2015
https://doi.org/10.3233/SW-140143
Sun HSrivatsa MTan SLi YKaplan LTao SYan XMacskassy SPerlich CLeskovec JWang WGhani R(2014)Analyzing expert behaviors in collaborative networksProceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining10.1145/2623330.2623722(1486-1495)Online publication date: 24-Aug-2014
https://dl.acm.org/doi/10.1145/2623330.2623722
Zhao JZhang XCao GSrivatsa MYan X(2014)Expertise-Based Data Access in Content-Centric Mobile Opportunistic NetworksProceedings of the 2014 IEEE 11th International Conference on Mobile Ad Hoc and Sensor Systems10.1109/MASS.2014.31(199-207)Online publication date: 28-Oct-2014
https://dl.acm.org/doi/10.1109/MASS.2014.31
Helic DStrohmaier MGranitzer MScherer RStumme GHotho A(2013)Models of human navigation in information networks based on decentralized searchProceedings of the 24th ACM Conference on Hypertext and Social Media10.1145/2481492.2481502(89-98)Online publication date: 1-May-2013
https://dl.acm.org/doi/10.1145/2481492.2481502
Saini HDev Sharma KPanda T(2013)Enhanced cooperative agent framework with security and increased performance of the computer network2013 International Conference on Information Systems and Computer Networks10.1109/ICISCON.2013.6524201(191-194)Online publication date: Mar-2013
https://doi.org/10.1109/ICISCON.2013.6524201

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