tutorial

Predicting information diffusion on social networks with partial knowledge

Authors:

Anis Najar,

Ludovic Denoyer,

Patrick GallinariAuthors Info & Claims

WWW '12 Companion: Proceedings of the 21st International Conference on World Wide Web

Pages 1197 - 1204

https://doi.org/10.1145/2187980.2188261

Published: 16 April 2012 Publication History

Get Access

Abstract

Models of information diffusion and propagation over large social media usually rely on a Close World Assumption: information can only propagate onto the network relational structure, it cannot come from external sources, the network structure is supposed fully known by the model. These assumptions are nonrealistic for many propagation processes extracted from Social Websites. We address the problem of predicting information propagation when the network diffusion structure is unknown and without making any closed world assumption. Instead of modeling a diffusion process, we propose to directly predict the final propagation state of the information over a whole user set. We describe a general model, able to learn predicting which users are the most likely to be contaminated by the information knowing an initial state of the network. Different instances are proposed and evaluated on artificial datasets.

References

[1]

Meeyoung Cha, Hamed Haddadi, Fabricio Benevenuto, and Krishna P. Gummadi. Measuring User Influence in Twitter: The Million Follower Fallacy. In In Proceedings of the 4th International AAAI Conference on Weblogs and Social Media (ICWSM).

Google Scholar

[2]

Wei Chen, Alex Collins, Rachel Cummings, Te Ke, Zhenming Liu, David Rincón, Xiaorui Sun, Yajun Wang, Wei Wei, and Yifei Yuan. Influence maximization in social networks when negative opinions may emerge and propagate. In SDM, pages 379--390, 2011.

Crossref

Google Scholar

[3]

David Kempe, Jon Kleinberg, and Éva Tardos. Maximizing the spread of influence through a social network. In Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '03, page 137, New York, New York, USA, August 2003. ACM Press.

Digital Library

Google Scholar

[4]

Masahiro Kimura, Kazumi Saito, Kouzou Ohara, and Hiroshi Motoda. Learning information diffusion model in a social network for predicting influence of nodes. Intell. Data Anal., 15(4):633--652, 2011.

Digital Library

Google Scholar

[5]

Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze. Introduction to information retrieval. Cambridge University Press, 2008.

Digital Library

Google Scholar

[6]

Seth A. Myers and Jure Leskovec. On the convexity of latent social network inference. In NIPS, pages 1741--1749, 2010.

Google Scholar

[7]

Manuel Gomez Rodriguez, David Balduzzi, and Bernhard Schölkopf. Uncovering the temporal dynamics of diffusion networks. In Lise Getoor and Tobias Scheffer, editors, Proceedings of the 28th International Conference on Machine Learning (ICML-11), ICML '11, pages 561--568, New York, NY, USA, June 2011. ACM.

Google Scholar

[8]

Kazumi Saito, Ryohei Nakano, and Masahiro Kimura. Prediction of information diffusion probabilities for independent cascade model. In KES (3), pages 67--75, 2008.

Digital Library

Google Scholar

[9]

Kazumi Saito, Kouzou Ohara, Yuki Yamagishi, Masahiro Kimura, and Hiroshi Motoda. Learning diffusion probability based on node attributes in social networks. In Marzena Kryszkiewicz, Henryk Rybinski, Andrzej Skowron, and Zbigniew W. Ras, editors, ISMIS, volume 6804 of Lecture Notes in Computer Science, pages 153--162. Springer, 2011.

Digital Library

Google Scholar

[10]

Jaewon Yang and Jure Leskovec. Modeling information diffusion in implicit networks. In ICDM, pages 599--608, 2010.

Digital Library

Google Scholar

Cited By

View all

Xiao YLi XZhang QLv RLi QWang R(2024)Spreading Mosaic: An Image Restoration-Inspired Social Rumor Propagation ModelIEEE Transactions on Multimedia10.1109/TMM.2023.330509526(2906-2917)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TMM.2023.3305095
Ramezani MAhadinia AZiaei Bideh ARabiee H(2023)Joint Inference of Diffusion and Structure in Partially Observed Social Networks Using Coupled Matrix FactorizationACM Transactions on Knowledge Discovery from Data10.1145/359923717:9(1-28)Online publication date: 18-Jul-2023
https://dl.acm.org/doi/10.1145/3599237
Mashayekhi YRezvanian AVahidipour S(2023)A novel regularized weighted estimation method for information diffusion prediction in social networksApplied Network Science10.1007/s41109-023-00605-z8:1Online publication date: 30-Nov-2023
https://doi.org/10.1007/s41109-023-00605-z
Show More Cited By

Index Terms

Predicting information diffusion on social networks with partial knowledge
1. Computing methodologies

Recommendations

Learning social network embeddings for predicting information diffusion
WSDM '14: Proceedings of the 7th ACM international conference on Web search and data mining

Analyzing and modeling the temporal diffusion of information on social media has mainly been treated as a diffusion process on known graphs or proximity structures. The underlying phenomenon results however from the interactions of several actors and ...
Diffusion in Social and Information Metworks: Research Problems; Probabilistic Models & Machine Learning Methods
WWW '15 Companion: Proceedings of the 24th International Conference on World Wide Web

In recent years, there has been an increasing effort on developing realistic models, and learning and inference algorithms to understand, predict, and influence diffusion over networks. This has been in part due to the increasing availability and ...
Centrality, gossip, and diffusion of information in networks
EC '14: Proceedings of the fifteenth ACM conference on Economics and computation

How can we identify the most influential nodes in a network for initiating diffusion? Are people able to easily identify those people in their communities who are best at spreading information, and if so How? Using theory and recent data, we will ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

WWW '12 Companion: Proceedings of the 21st International Conference on World Wide Web

April 2012

1250 pages

ISBN:9781450312301

DOI:10.1145/2187980

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

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]

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

Tutorial

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

19
Total Citations
View Citations
418
Total Downloads

Downloads (Last 12 months)4
Downloads (Last 6 weeks)1

Reflects downloads up to 16 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Xiao YLi XZhang QLv RLi QWang R(2024)Spreading Mosaic: An Image Restoration-Inspired Social Rumor Propagation ModelIEEE Transactions on Multimedia10.1109/TMM.2023.330509526(2906-2917)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TMM.2023.3305095
Ramezani MAhadinia AZiaei Bideh ARabiee H(2023)Joint Inference of Diffusion and Structure in Partially Observed Social Networks Using Coupled Matrix FactorizationACM Transactions on Knowledge Discovery from Data10.1145/359923717:9(1-28)Online publication date: 18-Jul-2023
https://dl.acm.org/doi/10.1145/3599237
Mashayekhi YRezvanian AVahidipour S(2023)A novel regularized weighted estimation method for information diffusion prediction in social networksApplied Network Science10.1007/s41109-023-00605-z8:1Online publication date: 30-Nov-2023
https://doi.org/10.1007/s41109-023-00605-z
Tian XQiu LZhang J(2021)User behavior prediction via heterogeneous information in social networksInformation Sciences: an International Journal10.1016/j.ins.2021.10.018581:C(637-654)Online publication date: 1-Dec-2021
https://dl.acm.org/doi/10.1016/j.ins.2021.10.018
Tchakounté FFaissal AAtemkeng MNtyam A(2020)A Reliable Weighting Scheme for the Aggregation of Crowd Intelligence to Detect Fake NewsInformation10.3390/info1106031911:6(319)Online publication date: 12-Jun-2020
https://doi.org/10.3390/info11060319
Amelkin VBogdanov PSingh A(2019)A Distance Measure for the Analysis of Polar Opinion Dynamics in Social NetworksACM Transactions on Knowledge Discovery from Data10.1145/333216813:4(1-34)Online publication date: 8-Aug-2019
https://dl.acm.org/doi/10.1145/3332168
Zhuo WZhao YZhan QLiu Y(2019)DiffusionGAN: Network Embedding for Information Diffusion Prediction with Generative Adversarial Nets2019 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom)10.1109/ISPA-BDCloud-SustainCom-SocialCom48970.2019.00120(808-816)Online publication date: Dec-2019
https://doi.org/10.1109/ISPA-BDCloud-SustainCom-SocialCom48970.2019.00120
Piskorec MSmuc TSikic M(2019)Disentangling Sources of Influence in Online Social NetworksIEEE Access10.1109/ACCESS.2019.29407627(131692-131704)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2940762
Kilanioti IFernández-Montes AFernández-Cerero DKarageorgos AMettouris CNejkovic VAlbanis NBashroush RPapadopoulos G(2019)Towards Efficient and Scalable Data-Intensive Content Delivery: State-of-the-Art, Issues and ChallengesHigh-Performance Modelling and Simulation for Big Data Applications10.1007/978-3-030-16272-6_4(88-137)Online publication date: 26-Mar-2019
https://doi.org/10.1007/978-3-030-16272-6_4
Gupta YIyengar SSaxena ADas D(2018)Modeling memetics using edge diversitySocial Network Analysis and Mining10.1007/s13278-018-0546-69:1Online publication date: 3-Dec-2018
https://doi.org/10.1007/s13278-018-0546-6
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.

Cited By

Index Terms

Recommendations

Learning social network embeddings for predicting information diffusion

Diffusion in Social and Information Metworks: Research Problems; Probabilistic Models & Machine Learning Methods

Centrality, gossip, and diffusion of information in networks