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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

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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.

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

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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]

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

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 April 2012

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WWW 2012

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Univ. de Lyon

WWW 2012: 21st World Wide Web Conference 2012

April 16 - 20, 2012

Lyon, France

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

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Cited By

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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
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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
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