research-article

OASNET: an optimal allocation approach to influence maximization in modular social networks

Authors:

Tianyu Cao,

Xindong Wu,

Song Wang,

Xiaohua HuAuthors Info & Claims

SAC '10: Proceedings of the 2010 ACM Symposium on Applied Computing

Pages 1088 - 1094

https://doi.org/10.1145/1774088.1774314

Published: 22 March 2010 Publication History

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Abstract

Influence maximization in a social network is to target a given number of nodes in the network such that the expected number of activated nodes from these nodes is maximized. A social network usually exhibits some degree of modularity. Previous research efforts that made use of this topological property are restricted to random networks with two communities. In this paper, we firstly transform the influence maximization problem in a modular social network to an optimal resource allocation problem in the same network. We assume that the communities of the social network are disconnected. We then propose a recursive relation for finding such an optimal allocation. We prove that finding an optimal allocation in a modular social network is NP-hard and propose a new optimal dynamic programming algorithm to solve the problem. We name our new algorithm OASNET (Optimal Allocation in a Social NETwork). We compare OASNET with equal allocation, proportional allocation, random allocation and selecting top degree nodes without any allocation strategy on both synthetic and real world datasets. Experimental results show that OASNET outperforms these four heuristics.

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Qing SGuo H(2024)Community-Diversified Influence Maximization Based on Community-Aware Closeness2024 5th International Conference on Artificial Intelligence and Computer Engineering (ICAICE)10.1109/ICAICE63571.2024.10864288(1178-1185)Online publication date: 8-Nov-2024
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Pan KQiu WChen W(2023)An Adaptive Community-Based Influence Maximization Algorithm in Social Networks2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC53992.2023.10394427(1813-1819)Online publication date: 1-Oct-2023
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Index Terms

OASNET: an optimal allocation approach to influence maximization in modular social networks
1. Information systems
  1. Information systems applications
    1. Data mining

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Published In

SAC '10: Proceedings of the 2010 ACM Symposium on Applied Computing

March 2010

2712 pages

ISBN:9781605586397

DOI:10.1145/1774088

Conference Chairs:
Sung Y. Shin
South Dakota State University
,
Sascha Ossowski
University Rey Juan Carlos, Spain
,
Michael Schumacher
University of Applied Sciences Western Switzerland, Switzerland
,
Program Chairs:
Mathew J. Palakal
Indiana University Purdue University
,
Chih-Cheng Hung
Southern Polytechnic State University

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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Association for Computing Machinery

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Published: 22 March 2010

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SAC'10: The 2010 ACM Symposium on Applied Computing

March 22 - 26, 2010

Sierre, Switzerland

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SAC '10 Paper Acceptance Rate 364 of 1,353 submissions, 27%;

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

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Abyaneh FCharkari NRoayaei M(2025)A community-based simulated annealing approach with a new structure-based neighborhood search to identify influential nodes in social networksSoft Computing10.1007/s00500-025-10490-629:3(1567-1585)Online publication date: 17-Feb-2025
https://doi.org/10.1007/s00500-025-10490-6
Qing SGuo H(2024)Community-Diversified Influence Maximization Based on Community-Aware Closeness2024 5th International Conference on Artificial Intelligence and Computer Engineering (ICAICE)10.1109/ICAICE63571.2024.10864288(1178-1185)Online publication date: 8-Nov-2024
https://doi.org/10.1109/ICAICE63571.2024.10864288
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Li YLi PHan LJiang ZWang ZWu Z(2023)Maximizing the Influence of Social Networks Based on Graph Attention Networks2023 3rd Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS)10.1109/ACCTCS58815.2023.00054(396-403)Online publication date: Feb-2023
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Zhang LLiu YYang HCheng FLiu QZhang X(2023)Overlapping community‐based particle swarm optimization algorithm for influence maximization in social networksCAAI Transactions on Intelligence Technology10.1049/cit2.12158Online publication date: 23-Jan-2023
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Wang ZChen XLi XDu YLan X(2022)Influence maximization based on network representation learning in social networkIntelligent Data Analysis10.3233/IDA-21614926:5(1321-1340)Online publication date: 1-Jan-2022
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Maximizing influence spread in modular social networks by optimal resource allocation

Optimal Sampling Laws for Stochastically Constrained Simulation Optimization on Finite Sets

Region Water Resources Optimal Allocation Based on Multi-Objective Genetic Algorithm

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