A fast algorithm for finding most influential people based on the linear threshold model

Rahimkhani, Khadije; AleAhmad, Abolfazl; Rahgozar, Maseud; Moeini, Ali

doi:10.1016/j.eswa.2014.09.037

Cited by 94 publications

(31 citation statements)

References 27 publications

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“…Moreover, they also study two most popular information propagation models in social networks, i.e. independent cascade model [14], [23] and linear threshold model [24], [25], and propose a ''hill-climbing'' greedy algorithm to solve this problem based on the two models. And experimental results show that this algorithm is much better than degree-based heuristic algorithms in terms of result accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…Then, this algorithm finds out ancestor nodes of all source nodes, and calculates the maximum active probability between ancestor nodes and source nodes (lines 3-29). Specifically, for each source node, ancestor nodes of this node are sought by utilizing Dijkstra algorithm and maximum active probability between them is calculated (lines [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]. In this process, visit is used to mark whether a node has been visited.…”

Section: A: Computation Of Ancestor Nodes and Maximum Active Probabilitymentioning

confidence: 99%

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LGIM: A Global Selection Algorithm Based on Local Influence for Influence Maximization in Social Networks

et al. 2020

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Influence maximization is to select k nodes from social networks to maximize the expected number of nodes activated by these selected nodes. Influence maximization problem plays a vital role in commercial marketing, news propagation, rumor control and public services. However, the existing algorithms for influence maximization usually tend to select one aspect from efficiency and accuracy as its main improving objective. This method of excessively pursuing one metric often leads to performing poorly in other metrics. Hence, we think that algorithms for influence maximization should make a suitable compromise between computation efficiency and result accuracy instead of excessively pursuing for one metric. Based on the above understanding, this paper proposes a new algorithm, called Global Selection Based on Local Influence (LGIM). The basic idea of the proposed algorithm is following: if a node can influence another node with large influence, the node also has large influence. Therefore, a two-stage filtering strategy of candidate nodes is proposed, which can reduce a large number of running time. Moreover, this paper also proposes a new objective function to estimate the influence spread of a node set. In summarize, the proposed algorithm utilizes the two-stage filtering strategy of candidate nodes to avoid unnecessary computation, and adopts a new objective function to replace time-consuming Monte-Carle simulations. Experimental results on six real-world social networks demonstrate that the proposed algorithm outperforms other four comparison algorithms when comprehensively considering computation efficiency and result accuracy.

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Section: Related Workmentioning

confidence: 99%

Section: A: Computation Of Ancestor Nodes and Maximum Active Probabilitymentioning

confidence: 99%

LGIM: A Global Selection Algorithm Based on Local Influence for Influence Maximization in Social Networks

et al. 2020

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“…The CELF++ method can further improve the computational efficiency, which is reported to be about 35%-55% faster than CELF method. Motivated by the existence of community in a network, several work [8,36,41] have focused on employing the communities of the network, and proposing more efficient algorithms to speed up the seed selection. Additionally, instead of Monte Carlo simulation-based method, various heuristic algorithms have also been proposed to improve the efficiency in evaluating the influence spread for a given seed set in the greedy algorithm.…”

Section: Related Work 21 Influence Maximizationmentioning

confidence: 99%

Maximizing Boosted Influence Spread with Edge Addition in Online Social Networks

Yuan

2020

ACM/IMS Trans. Data Sci.

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Influence maximization with application to viral marketing is a well-studied problem of finding a small number of influential users in a social network to maximize the spread of influence under certain influence cascade models. However, almost all previous studies have focused on node-level mining, where they consider identifying nodes as the initial seeders to achieve the desired outcomes. In this article, instead of targeting nodes, we investigate a new boosted influence maximization problem from the edge-level perspective, which asks for finding an edge set that is added to the network to maximize the increased influence spread of a given seed set. We show that the problem is NP-hard and the influence spread function no longer exhibits the property of submodularity, which impose more challenging on the problem. Therefore, we devise a restricted form that is submodular and propose a greedy algorithm with approximate guarantee to solve the problem. However, because of its poor computational efficiency, we further propose an improved greedy algorithm that integrates several effective optimization strategies to significantly speed up the edge selection without sacrificing its accuracy. Extensive experiments over real-world available social networks of different sizes demonstrate the effectiveness and efficiency of the proposed methods. CCS Concepts: • Information systems → Data mining; Web applications; • Theory of computation → Design and analysis of algorithms;

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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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A fast algorithm for finding most influential people based on the linear threshold model

Cited by 94 publications

References 27 publications

LGIM: A Global Selection Algorithm Based on Local Influence for Influence Maximization in Social Networks

LGIM: A Global Selection Algorithm Based on Local Influence for Influence Maximization in Social Networks

Maximizing Boosted Influence Spread with Edge Addition in Online Social Networks

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

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