research-article

A probability based algorithm for influence maximization in social networks

Authors:

Sanglu LuAuthors Info & Claims

Internetware '13: Proceedings of the 5th Asia-Pacific Symposium on Internetware

Article No.: 12, Pages 1 - 7

https://doi.org/10.1145/2532443.2532455

Published: 23 October 2013 Publication History

Abstract

In a social network, information runs from word-of-mouth based on the relationship of the users. The influence maximization is to find a limited number of initial users (nodes) to spread the information, so that the maximum number of other users could accept the information, which is a useful technique for marketing, information monitoring and advertising in a social network. Diffusion model of social networks imitates the process of information spreading in social networks, and Independent Cascade (IC) Model and Linear Threshold (LT) Model, are well-known stochastic information influence models. In this paper, we extend the classical IC model according to the observation of users' behaviors in social networks and propose an effective influence maximization algorithm based on this extended IC model. This novel algorithm calculates the influence probability of each node in sub-graphs that other nodes can engendered to it iteratively. The simulation experiments on real social network datasets show that our algorithm is much faster than the greedy hill-climbing algorithm, while the results are very close to the greedy algorithm and out-perform the other heuristic algorithms.

References

[1]

Kempe D, Kleinberg J, Tardos É. Maximizing the spread of influence through a social network{C}//Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2003: 137--146.

Digital Library

[2]

Leskovec J, Krause A, Guestrin C, et al. Cost-effective outbreak detection in networks{C}//Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2007: 420--429.

Digital Library

[3]

Chen W, Wang Y, Yang S. Efficient influence maximization in social networks{C}//Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2009: 199--208. New York, NY, 526--531.

Digital Library

[4]

Bhagat S, Goyal A, Lakshmanan L V S. Maximizing product adoption in social networks{C}//Proceedings of the fifth ACM international conference on Web search and data mining. ACM, 2012: 603--612.

Digital Library

[5]

Chen W, Wang C, Wang Y. Scalable influence maximization for prevalent viral marketing in large-scale social networks{C}//Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2010: 1029--1038.

Digital Library

[6]

Young H P. The diffusion of innovations in social networks{J}. Economy as an Evolving Complex System. Proceedings volume in the Santa Fe Institute studies in the sciences of complexity, 2002, 3: 267--282.

[7]

Chen W, Wang C, Wang Y. Scalable influence maximization for prevalent viral marketing in large-scale social networks{C}//Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2010: 1029--1038.

Digital Library

[8]

Goyal A, Lu W, Lakshmanan L V S. Simpath: An efficient algorithm for influence maximization under the linear threshold model{C}//Data Mining (ICDM), 2011 IEEE 11th International Conference on. IEEE, 2011: 211--220.

Digital Library

[9]

Hartline J, Mirrokni V, Sundararajan M. Optimal marketing strategies over social networks{C}//Proceedings of the 17th international conference on World Wide Web. ACM, 2008: 189--198.

Digital Library

[10]

Carnes T, Nagarajan C, Wild S M, et al. Maximizing influence in a competitive social network: a follower's perspective{C}//Proceedings of the ninth international conference on Electronic commerce. ACM, 2007: 351--360.

Digital Library

[11]

Goyal A, Bonchi F, Lakshmanan L V S. Learning influence probabilities in social networks{C}//Proceedings of the third ACM international conference on Web search and data mining. ACM, 2010: 241--250.

Digital Library

[12]

Ou G, Chen W, Wang T, et al. Sentiment Influence Maximization Model for Microblogging System{J}. Jisuanji Kexue yu Tansuo, 2012, 6(9): 769--778.

[13]

Song X, Chi Y, Hino K, et al. Information flow modeling based on diffusion rate for prediction and ranking{C}//Proceedings of the 16th international conference on World Wide Web. ACM, 2007: 191--200.

Digital Library

[14]

Cha M, Mislove A, Gummadi K P. A measurement-driven analysis of information propagation in the flickr social network{C}//Proceedings of the 18th international conference on World wide web. ACM, 2009: 721--730.

Digital Library

[15]

Goyal A, Lu W, Lakshmanan L V S. Celf++: optimizing the greedy algorithm for influence maximization in social networks{C}//Proceedings of the 20th international conference companion on World wide web. ACM, 2011: 47--48.

Digital Library

[16]

Kimura M, Saito K. Tractable models for information diffusion in social networks{M}//Knowledge Discovery in Databases: PKDD 2006. Springer Berlin Heidelberg, 2006: 259--271.

Digital Library

[17]

Hartline J, Mirrokni V, Sundararajan M. Optimal marketing strategies over social networks{C}//Proceedings of the 17th international conference on World Wide Web. ACM, 2008: 189--198.

Digital Library

[18]

Tang J, Sun J, Wang C, et al. Social influence analysis in large-scale networks{C}//Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2009: 807--816.

Digital Library

[19]

Budak C, Agrawal D, El Abbadi A. Limiting the spread of misinformation in social networks{C}//Proceedings of the 20th international conference on World wide web. ACM, 2011: 665--674.

Digital Library

[20]

Wasserman S. Social network analysis: Methods and applications{M}. Cambridge university press, 1994.

[21]

Richardson M, Domingos P. Mining knowledge-sharing sites for viral marketing{C}//Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2002: 61--70.

Digital Library

[22]

Goldenberg J, Libai B, Muller E. Using complex systems analysis to advance marketing theory development: Modeling heterogeneity effects on new product growth through stochastic cellular automata{J}. Academy of Marketing Science Review, 2001, 9(3): 1--18.

[23]

Domingos P, Richardson M. Mining the network value of customers{C}//Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2001: 57--66.

Digital Library

Cited By

Hai-Jew S(2022)Self-Presenting Virtually for Remote Social InfluencePractical Peer-to-Peer Teaching and Learning on the Social Web10.4018/978-1-7998-6496-7.ch013(407-461)Online publication date: 2022
https://doi.org/10.4018/978-1-7998-6496-7.ch013
N. SB. ABhattacharya S(2018)Influence maximization in large social networks: Heuristics, models and parametersFuture Generation Computer Systems10.1016/j.future.2018.07.01589(777-790)Online publication date: Dec-2018
https://doi.org/10.1016/j.future.2018.07.015
Sumith NAnnappa BBhattacharya S(2017)A Holistic Approach to Influence MaximizationHybrid Intelligence for Social Networks10.1007/978-3-319-65139-2_6(129-160)Online publication date: 11-Sep-2017
https://doi.org/10.1007/978-3-319-65139-2_6
Show More Cited By

Index Terms

A probability based algorithm for influence maximization in social networks
1. Theory of computation
  1. Computational complexity and cryptography
    1. Complexity classes

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Published In

cover image ACM Other conferences

Internetware '13: Proceedings of the 5th Asia-Pacific Symposium on Internetware

October 2013

211 pages

ISBN:9781450323697

DOI:10.1145/2532443

Conference Chairs:
Hong Mei,
Jian Lv,
Program Chair:
Xiaoguang Mao

Copyright © 2013 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]

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NJU: Nanjing University
CCF: China Computer Federation
Chinese Academy of Sciences

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Association for Computing Machinery

New York, NY, United States

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Published: 23 October 2013

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Internetware '13

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CCF

Internetware '13: The Fifth Asia-Pacific Symposium on Internetware

October 23 - 24, 2013

Changsha, China

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Internetware '13 Paper Acceptance Rate 15 of 50 submissions, 30%;

Overall Acceptance Rate 55 of 111 submissions, 50%

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Cited By

Hai-Jew S(2022)Self-Presenting Virtually for Remote Social InfluencePractical Peer-to-Peer Teaching and Learning on the Social Web10.4018/978-1-7998-6496-7.ch013(407-461)Online publication date: 2022
https://doi.org/10.4018/978-1-7998-6496-7.ch013
N. SB. ABhattacharya S(2018)Influence maximization in large social networks: Heuristics, models and parametersFuture Generation Computer Systems10.1016/j.future.2018.07.01589(777-790)Online publication date: Dec-2018
https://doi.org/10.1016/j.future.2018.07.015
Sumith NAnnappa BBhattacharya S(2017)A Holistic Approach to Influence MaximizationHybrid Intelligence for Social Networks10.1007/978-3-319-65139-2_6(129-160)Online publication date: 11-Sep-2017
https://doi.org/10.1007/978-3-319-65139-2_6
Pagare RKhare A(2016)Churn prediction by finding most influential nodes in social network2016 International Conference on Computing, Analytics and Security Trends (CAST)10.1109/CAST.2016.7914942(68-71)Online publication date: Dec-2016
https://doi.org/10.1109/CAST.2016.7914942
Kaur HHe J(2015)Blocking negative influential node set in social networks: from host perspectiveTransactions on Emerging Telecommunications Technologies10.1002/ett.300728:4Online publication date: 28-Dec-2015
https://doi.org/10.1002/ett.3007

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