Community-based influence maximization in social networks under a competitive linear threshold model

Bozorgi, Arastoo; Samet, Saeed; Kwisthout, Johan; Wareham, Todd

doi:10.1016/j.knosys.2017.07.029

Cited by 110 publications

(40 citation statements)

References 34 publications

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“…The influence maximization is a very relevant problem to the influential node identification. It aims at finding a subset of key users that maximize their influence spread over a social network [24,55]. At present, a series of algorithms have been developed for this problem.…”

Section: Related Workmentioning

confidence: 99%

A Comprehensive Algorithm for Evaluating Node Influences in Social Networks Based on Preference Analysis and Random Walk

Mao

Xiao

2018

Complexity

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In the era of big data, social network has become an important reflection of human communications and interactions on the Internet. Identifying the influential spreaders in networks plays a crucial role in various areas, such as disease outbreak, virus propagation, and public opinion controlling. Based on the three basic centrality measures, a comprehensive algorithm named PARW-Rank for evaluating node influences has been proposed by applying preference relation analysis and random walk technique. For each basic measure, the preference relation between every node pair in a network is analyzed to construct the partial preference graph (PPG). Then, the comprehensive preference graph (CPG) is generated by combining the preference relations with respect to three basic measures. Finally, the ranking of nodes is determined by conducting random walk on the CPG. Furthermore, five public social networks are used for comparative analysis. The experimental results show that our PARW-Rank algorithm can achieve the higher precision and better stability than the existing methods with a single centrality measure.

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Section: Related Workmentioning

confidence: 99%

A Comprehensive Algorithm for Evaluating Node Influences in Social Networks Based on Preference Analysis and Random Walk

Mao

Xiao

2018

Complexity

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“…The existing studies on the diffusion models of influence maximization are mainly classified into epidemic model() and game‐theoretic model. () Epidemic‐based diffusion model usually includes Systemic Inflammatory Response Syndrome, Ising model, linear threshold model (LT),() and independent cascade (IC) model.…”

Section: Background and Related Workmentioning

confidence: 99%

“…() Epidemic‐based diffusion model usually includes Systemic Inflammatory Response Syndrome, Ising model, linear threshold model (LT),() and independent cascade (IC) model. () In Kempe et al, influence maximization problem based on LT and IC models was explored. In both LT and IC models, each node had only one chance to activate its neighbors.…”

Section: Background and Related Workmentioning

confidence: 99%

An adaptive approach for handling two‐dimension influence maximization in social networks

Wang

Huang

Cai

et al. 2018

Int J Communication

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To synthetically and dynamically make strategic choices in social networks, a novel adaptive approach to deal with two-dimension influence maximization problem (TIMP) is proposed with game-based diffusion model, which can achieve trade-off between diffusion time and the number of active nodes. At first, TIMP model is synthetically formulated, and diffusion time and the number of active nodes are defined mathematically. In particular, budget efficiency is presented to describe TIMP in order that an appropriate trade-off between diffusion time and the number of active nodes can be reached. Then, an adaptive heuristic (A-Heuristic) scheme is proposed to dynamically determine the initial seed nodes (ie, the most influential nodes). Finally, experiments are performed, and their results verify the superior performance of the proposed scheme in terms of budget efficiency, running time, and the number of active nodes.

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“…for any A ⊆ B ⊆ U, u ∈ U \ B. Based on that, they applied the classic hill-climbing algorithm, which provides a napproximation ratio of (1 − 1/e) to solve CIM problem [12][13][14][15][16][17]. For example, Lu et al [12] studied the problem in context of fair competitive influence from the host perspective.…”

Section: Introductionmentioning

confidence: 99%

“…Chen et al [13] proposed an independent cascade model with negative opinions (IC-N) model by extending IC model and showed a greedy algorithm with the approximation ratio of 1 − 1/e. Recently, some works have addressed the problem in other directions, including proposing heuristic algorithm [15] and studying some variants of CIM [16,18,19]. Although previous works try to solve the CIM problem in many circumstances, the feasibility of the existing works is limited for following reasons.…”

Section: Introductionmentioning

confidence: 99%

Competitive Influence Maximization within Time and Budget Constraints in Online Social Networks: An Algorithmic Approach

Pham¹,

Duong²,

Hoang³

et al. 2019

Applied Sciences

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Competitive Influence Maximization ( CIM ) problem, which seeks a seed set nodes of a player or a company to propagate their product’s information while at the same time their competitors are conducting similar strategies, has been paid much attention recently due to its application in viral marketing. However, existing works neglect the fact that the limited budget and time constraints can play an important role in competitive influence strategy of each company. In addition, based on the the assumption that one of the competitors dominates in the competitive influence process, the majority of prior studies indicate that the competitive influence function (objective function) is monotone and submodular.This led to the fact that CIM can be approximated within a factor of 1 − 1 / e − ϵ by a Greedy algorithm combined with Monte Carlo simulation method. Unfortunately, in a more realistic scenario where there is fair competition among competitors, the objective function is no longer submodular. In this paper, we study a general case of CIM problem, named Budgeted Competitive Influence Maximization ( BCIM ) problem, which considers CIM with budget and time constraints under condition of fair competition. We found that the objective function is neither submodular nor suppermodular. Therefore, it cannot admit Greedy algorithm with approximation ratio of 1 − 1 / e . We propose Sandwich Approximation based on Polling-Based Approximation ( SPBA ), an approximation algorithm based on Sandwich framework and polling-based method. Our experiments on real social network datasets showed the effectiveness and scalability of our algorithm that outperformed other state-of-the-art methods. Specifically, our algorithm is scalable with million-scale networks in only 1.5 min.

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Community-based influence maximization in social networks under a competitive linear threshold model

Cited by 110 publications

References 34 publications

A Comprehensive Algorithm for Evaluating Node Influences in Social Networks Based on Preference Analysis and Random Walk

A Comprehensive Algorithm for Evaluating Node Influences in Social Networks Based on Preference Analysis and Random Walk

An adaptive approach for handling two‐dimension influence maximization in social networks

Competitive Influence Maximization within Time and Budget Constraints in Online Social Networks: An Algorithmic Approach

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