Article

Tractable models for information diffusion in social networks

Authors:

Masahiro Kimura,

Kazumi SaitoAuthors Info & Claims

ECMLPKDD'06: Proceedings of the 10th European Conference on Principles and Practice of Knowledge Discovery in Databases

Pages 259 - 271

Published: 18 September 2006 Publication History

Abstract

When we consider the problem of finding influential nodes for information diffusion in a large-scale social network based on the Independent Cascade Model (ICM), we need to compute the expected number of nodes influenced by a given set of nodes. However, a good estimate of this quantity needs a large amount of computation in the ICM. In this paper, we propose two natural special cases of the ICM such that a good estimate of this quantity can be efficiently computed. Using real large-scale social networks, we experimentally demonstrate that for extracting influential nodes, the proposed models can provide novel ranking methods that are different from the ICM, typical methods of social network analysis, and "PageRank" method. Moreover, we experimentally demonstrate that when the propagation probabilities through links are small, they can give good approximations to the ICM for finding sets of influential nodes.

References

[1]

Brin, S., Page, L.: The anatomy of a large-scale hypertextual Web search engine. In: Proc. WWW 1998, pp. 107-117 (1998)

Crossref

Google Scholar

[2]

Domingos, P., Richardson, M.: Mining the network value of customers. In: Proc. KDD 2001, pp. 57-66 (2001)

Crossref

Google Scholar

[3]

Goldenberg, K.J., Libai, B., Muller, E.: Talk of the network: A complex systems look at the underlying process of word-of-mouth. Marketing Letters 12, 211-223 (2001)

Google Scholar

[4]

Gruhl, D., Guha, R., Liben-Nowell, D., Tomkins, A.: Information diffusion through blogspace. In: Proc. WWW 2004, pp. 491-501 (2004)

Crossref

Google Scholar

[5]

Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: Proc. KDD 2003, pp. 137-146 (2003)

Crossref

Google Scholar

[6]

Leskovec, J., Singh, A., Kleinberg, J.: Patterns of influence in a recommendation network. In: Ng, W.-K., Kitsuregawa, M., Li, J., Chang, K. (eds.) PAKDD 2006. LNCS, vol. 3918, pp. 380-389. Springer, Heidelberg (2006)

Crossref

Google Scholar

[7]

McCallum, A., Corrada-Emmanuel, A., Wang, X.: Topic and role discovery in social networks. In: Proc. IJCAI 2005, pp. 786-791 (2005)

Crossref

Google Scholar

[8]

Nemhauser, G.L., Wolsey, L.A.: Integer and Combinatorial Optimization. Wiley, New York (1988)

Crossref

Google Scholar

[9]

Newman, M.E.J.: Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality. Physical Review E 64, 016132 (2001)

Google Scholar

[10]

Ng, A.Y., Zheng, A.X., Jordan, M.I.: Link analysis, eigenvectors and stability. In: Proc. IJCAI 2001, pp. 903-901 (2001)

Crossref

Google Scholar

[11]

Palla, G., Derényi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435, 814-818 (2005)

Google Scholar

[12]

Richardson, M., Domingos, P.: Mining knowledge-sharing sites for viral marketing. In: Proc. KDD 2002, pp. 61-70 (2002)

Crossref

Google Scholar

[13]

Wasserman, S., Faust, K.: Social Network Analysis. Cambridge University Press, Cambridge (1994)

Google Scholar

Cited By

View all

Shang JLi K(2018)Dynamic Update Upper Bounds Influence Maximization AlgorithmProceedings of the 2018 2nd International Conference on Computer Science and Artificial Intelligence10.1145/3297156.3297185(212-217)Online publication date: 8-Dec-2018
https://dl.acm.org/doi/10.1145/3297156.3297185
Chang BXu TLiu QChen E(2018)Study on Information Diffusion Analysis in Social Networks and Its ApplicationsInternational Journal of Automation and Computing10.1007/s11633-018-1124-015:4(377-401)Online publication date: 1-Aug-2018
https://dl.acm.org/doi/10.1007/s11633-018-1124-0
Chang CYeh MChuang K(2018)Node reactivation model to intensify influence on network targetsKnowledge and Information Systems10.1007/s10115-017-1071-z54:3(567-590)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1007/s10115-017-1071-z
Show More Cited By

Recommendations

Discovery of super-mediators of information diffusion in social networks
DS'10: Proceedings of the 13th international conference on Discovery science

We address the problem of discovering a different kind of influential nodes, which we call "super-mediator", i.e. those nodes which play an important role to pass the information to other nodes, and propose a method for discovering super-mediators from ...
Predicting information diffusion probabilities in social networks

Proposed approach predicts information diffusion probability in the social networks.A machine learning based Bayesian network model is trained and tested.Method utilizes diffusion history, network and content based features for modeling.Method utilizes ...
Anomalous information diffusion in social networks: Twitter and Digg
Highlights
- A new characteristic of diffusion type in social networks is introduced.
- ...
Abstract
Diffusion is an important phenomenon in different sciences like epidemiology, economy, biology and social sciences. Information diffusion is mainly important for two reasons, to study and forecast diffusion growth and to find the ...
Graphical abstract

Display Omitted

Comments

Information & Contributors

Information

Published In

ECMLPKDD'06: Proceedings of the 10th European Conference on Principles and Practice of Knowledge Discovery in Databases

September 2006

658 pages

ISBN:3540453741

Editors:
Johannes Fürnkranz
Technische Universität Darmstadt, Fachbereich Informatik, Darmstadt, Germany
,
Tobias Scheffer
Humboldt-Universität zu Berlin, Institut für Informatik, Berlin, Germany
,
Myra Spiliopoulou
Otto-von-Guericke-Universität Magdeburg, Fakultät für Informatik, Magdeburg, Germany

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 18 September 2006

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

43
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 25 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Shang JLi K(2018)Dynamic Update Upper Bounds Influence Maximization AlgorithmProceedings of the 2018 2nd International Conference on Computer Science and Artificial Intelligence10.1145/3297156.3297185(212-217)Online publication date: 8-Dec-2018
https://dl.acm.org/doi/10.1145/3297156.3297185
Chang BXu TLiu QChen E(2018)Study on Information Diffusion Analysis in Social Networks and Its ApplicationsInternational Journal of Automation and Computing10.1007/s11633-018-1124-015:4(377-401)Online publication date: 1-Aug-2018
https://dl.acm.org/doi/10.1007/s11633-018-1124-0
Chang CYeh MChuang K(2018)Node reactivation model to intensify influence on network targetsKnowledge and Information Systems10.1007/s10115-017-1071-z54:3(567-590)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1007/s10115-017-1071-z
Ho TBui QBui M(2018)Homophily Independent Cascade Diffusion Model Based on Textual InformationComputational Collective Intelligence10.1007/978-3-319-98443-8_13(134-145)Online publication date: 5-Sep-2018
https://dl.acm.org/doi/10.1007/978-3-319-98443-8_13
Angell RSchoenebeck G(2017)Don’t Be Greedy: Leveraging Community Structure to Find High Quality Seed Sets for Influence MaximizationWeb and Internet Economics10.1007/978-3-319-71924-5_2(16-29)Online publication date: 17-Dec-2017
https://dl.acm.org/doi/10.1007/978-3-319-71924-5_2
Zhang QWu YXie J(2017)Layer-Prioritized Influence Maximization in Social NetworksNeural Information Processing10.1007/978-3-319-70139-4_64(631-638)Online publication date: 14-Nov-2017
https://dl.acm.org/doi/10.1007/978-3-319-70139-4_64
Yadav AMarcolino LRice EPetering RWinetrobe HRhoades HTambe MCarmichael H(2016)PSINETAI Magazine10.1609/aimag.v37i2.263237:2(47-62)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.1609/aimag.v37i2.2632
Gomez-Rodriguez MSong LDu NZha HSchölkopf B(2016)Influence Estimation and Maximization in Continuous-Time Diffusion NetworksACM Transactions on Information Systems10.1145/282425334:2(1-33)Online publication date: 8-Feb-2016
https://dl.acm.org/doi/10.1145/2824253
Subbian KAggarwal CSrivastava J(2016)Mining Influencers Using Information Flows in Social StreamsACM Transactions on Knowledge Discovery from Data10.1145/281562510:3(1-28)Online publication date: 29-Jan-2016
https://dl.acm.org/doi/10.1145/2815625
Golnari GAmir ABanerjee AZhang Z(2015)Revisiting non-progressive influence modelsProceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence10.5555/3020847.3020881(316-325)Online publication date: 12-Jul-2015
https://dl.acm.org/doi/10.5555/3020847.3020881
Show More Cited By

Abstract

References

Cited By

Recommendations

Discovery of super-mediators of information diffusion in social networks

Predicting information diffusion probabilities in social networks

Anomalous information diffusion in social networks: Twitter and Digg

Comments

Information

Published In

Sponsors

Publisher

Publication History

Qualifiers

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

View options

Share

Share this Publication link

Share on social media

Affiliations