research-article

Holistic Influence Maximization: Combining Scalability and Efficiency with Opinion-Aware Models

Authors:

Sainyam Galhotra,

Shourya RoyAuthors Info & Claims

SIGMOD '16: Proceedings of the 2016 International Conference on Management of Data

Pages 743 - 758

https://doi.org/10.1145/2882903.2882929

Published: 14 June 2016 Publication History

Abstract

The steady growth of graph data from social networks has resulted in wide-spread research in finding solutions to the influence maximization problem. In this paper, we propose a holistic solution to the influence maximization (IM) problem. (1) We introduce an opinion-cum-interaction (OI) model that closely mirrors the real-world scenarios. Under the OI model, we introduce a novel problem of Maximizing the Effective Opinion (MEO) of influenced users. We prove that the MEO problem is NP-hard and cannot be approximated within a constant ratio unless P=NP. (2) We propose a heuristic algorithm OSIM to efficiently solve the MEO problem. To better explain the OSIM heuristic, we first introduce EaSyIM - the opinion-oblivious version of OSIM, a scalable algorithm capable of running within practical compute times on commodity hardware. In addition to serving as a fundamental building block for OSIM, EaSyIM is capable of addressing the scalability aspect - memory consumption and running time, of the IM problem as well. Empirically, our algorithms are capable of maintaining the deviation in the spread always within 5% of the best known methods in the literature. In addition, our experiments show that both OSIM and EaSyIM are effective, efficient, scalable and significantly enhance the ability to analyze real datasets.

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Tang JLi Y(2024)A hybrid dynamic memetic algorithm for the influence maximization problem in social networksInternational Journal of Modern Physics C10.1142/S0129183124501924Online publication date: 17-Aug-2024
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Cai TLei QSheng QCui NYang SYang JZhang WMahmood A(2024)Reconnecting the Estranged Relationships: Optimizing the Influence Propagation in Evolving NetworksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.3316268(1-14)Online publication date: 2024
https://doi.org/10.1109/TKDE.2023.3316268
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Index Terms

Holistic Influence Maximization: Combining Scalability and Efficiency with Opinion-Aware Models
1. Information systems
  1. Information systems applications
    1. Data mining
2. Mathematics of computing
  1. Discrete mathematics
    1. Graph theory
      1. Graph algorithms

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cover image ACM Conferences

SIGMOD '16: Proceedings of the 2016 International Conference on Management of Data

June 2016

2300 pages

ISBN:9781450335317

DOI:10.1145/2882903

General Chairs:
Fatma Özcan
IBM Research, USA
,
Georgia Koutrika
HP Labs, USA
,
Program Chair:
Sam Madden
Massachusetts Institute of Technology, USA

Copyright © 2016 ACM.

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Published: 14 June 2016

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Feng CChen XGuo QZhang FWang S(2024)Efficient Approximation Algorithms for Minimum Cost Seed Selection with Probabilistic Coverage GuaranteeProceedings of the ACM on Management of Data10.1145/36771332:4(1-26)Online publication date: 30-Sep-2024
https://doi.org/10.1145/3677133
Tang JLi Y(2024)A hybrid dynamic memetic algorithm for the influence maximization problem in social networksInternational Journal of Modern Physics C10.1142/S0129183124501924Online publication date: 17-Aug-2024
https://doi.org/10.1142/S0129183124501924
Cai TLei QSheng QCui NYang SYang JZhang WMahmood A(2024)Reconnecting the Estranged Relationships: Optimizing the Influence Propagation in Evolving NetworksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.3316268(1-14)Online publication date: 2024
https://doi.org/10.1109/TKDE.2023.3316268
Sakiyama TNakajima KAida M(2024)Efficient Intervention in the Spread of Misinformation in Social NetworksIEEE Access10.1109/ACCESS.2024.345983012(133489-133498)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3459830
Biswas TAbbasi AChakrabortty R(2024)A cost-effective seed selection model for multi-constraint influence maximization in social networksDecision Analytics Journal10.1016/j.dajour.2024.10047411(100474)Online publication date: Jun-2024
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Zhang KLi MTeng SLi LWang YZhao XDi JZhang J(2024)Maximizing influence via link prediction in evolving networksArray10.1016/j.array.2024.100366(100366)Online publication date: Sep-2024
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Göktürk GKaya K(2024)DiFuseR: a distributed sketch-based influence maximization algorithm for GPUsThe Journal of Supercomputing10.1007/s11227-024-06566-z81:1Online publication date: 17-Oct-2024
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Venunath MSujatha PKoti PDharavath S(2024)The Evolution of Influence Maximization Studies: A Scientometric AnalysisAccelerating Discoveries in Data Science and Artificial Intelligence II10.1007/978-3-031-51163-9_12(109-118)Online publication date: 14-May-2024
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Sun CWang ZRui XYu PSun L(2023)An in-depth study on key nodes in social networksIntelligent Data Analysis10.3233/IDA-22701827:6(1811-1838)Online publication date: 20-Nov-2023
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