Influence Maximization in Signed Social Networks

Shen, Chengguang; Nishide, Ryo; Piumarta, Ian; Takada, Hideyuki; Liang, Wenxin

doi:10.1007/978-3-319-26190-4_27

Cited by 11 publications

(3 citation statements)

References 24 publications

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“…Li et al [18] proposed a problem that maximizes the spread in a certain region in a location-aware social network. Shen et al [19] first studied the IM in the signed networks to consider negative opinions and they claimed that they are the first to study the IM problem in signed networks with opinion formation. Chen et al [17] used a simple model in which the nodes in the network are either negative or positive, which is too simplistic.…”

Section: Influence Maximization Problemsmentioning

confidence: 99%

Bi-Objective Influence Maximization with Consideration of Node Burden

Feng,

Zhao,

et al. 2024

Preprint

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Influence maximization aims at finding a set of nodes with the maximum influence in a network. Apart from the influence, the burden of nodes is worth considering as an objective but has been neglected in the literature. Herein, the burden refers to the capacity of a node to serve its influenced nodes, e.g., the storage of a device in the device-to-device networks. In this paper, we propose a new bi-objective influence maximization problem that jointly maximizes the influence of seed nodes and minimizes the variance of the burden of seed nodes, called Influence Maximization with Burden. We provide intuitive and empirical evidence to demonstrate the feasibility and necessity of the proposed problem. Furthermore, we develop two algorithms to solve the proposed influence maximization problem. One is a fast suboptimal algorithm based on the reverse reachable sampling method. The other is a memetic algorithm with a novel meme operator to identify promising nodes. The results of the experiments on synthesis and real networks illustrate that the proposed algorithms perform more effectively and efficiently than other existing algorithms. We published our codes on GitHub: https://github.com/fmyzckj.

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Section: Influence Maximization Problemsmentioning

confidence: 99%

Bi-Objective Influence Maximization with Consideration of Node Burden

Feng,

Zhao,

et al. 2024

Preprint

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“…With the prevalence of signed social networks, recent works investigated the problem of finding the seed set that maximizes positive influence, which is also known as positive influence maximization. [27], [36], [38] studied positive influence maximization under different extensions of IC and LT models. Li et al [28] explored similar problem in a signed social network with voter model.…”

Section: Sensitivity Analysis Wrt #Seeds and #Targetsmentioning

confidence: 99%

Maximizing Contrasting Opinions in Signed Social Networks

Rawal

Khan

2019

2019 IEEE International Conference on Big Data (Big Data)

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The classic influence maximization problem finds a limited number of influential seed users in a social network such that the expected number of influenced users in the network, following an influence cascade model, is maximized. The problem has been studied in different settings, with further generalization of the graph structure, e.g., edge weights and polarities, target user categories, etc. In this paper, we introduce a unique influence diffusion scenario involving a population that split into two distinct groups, with opposing views. We aim at finding the top-k influential seed nodes so to simultaneously maximize the adoption of two distinct, antithetical opinions in the two groups, respectively. Efficiently finding such influential users is essential in a wide range of applications such as increasing voter engagement and turnout, steering public debates and discussions on societal issues with contentious opinions. We formulate this novel problem with the voter model to simulate opinion diffusion and dynamics, and then design a linear-time and exact algorithm COSiNeMax, while also investigating the long-term opinion characteristics in the network. Our experiments with several real-world datasets demonstrate the effectiveness and efficiency of the proposed algorithm, compared to various baselines.

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“…The preliminary version of this article was presented at ''the 16th International Conference of Web Information Systems Engineering (WISE 2015)'' [20]. We extend the following contents in this article.…”

Section: Introductionmentioning

confidence: 98%

Influence Maximization in Signed Social Networks With Opinion Formation

Liang

Shen²,

et al. 2019

IEEE Access

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Influence maximization (IM) has been widely studied in recent years. Given fixed number of seed users and certain diffusion models, the IM problem aims to select proper seed users in a social networks such that they can achieve the maximal spread of influence. Most previous work assumes that there are only positive relationships between users, and thus users spread influence positively. However, negative relationships also universally exist in various social networks and are complementary to positive relationships in information diffusion. In this paper, the influence maximization problem is addressed in signed social networks that contain both positive and negative relationships. We propose a novel diffusion model called LT-S and two influence spread functions. The proposed LT-S model extends the classical linear threshold model with opinion formation that incorporates both positive and negative opinions and simulates information diffusion in real-world social networks. The influence spread functions under the LT-S model are neither monotone nor submodular which bring challenges to maximization. The RLP algorithm is proposed to tackle the issue, which is improved from R-Greedy algorithm by incorporating two proposed accelerating techniques, the live-edge based and propagation-path based techniques. The results of the extensive experiments on public real signed social network datasets demonstrate that our algorithm outperforms the baseline algorithms in terms of both efficiency and effectiveness.

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Influence Maximization in Signed Social Networks

Cited by 11 publications

References 24 publications

Bi-Objective Influence Maximization with Consideration of Node Burden

Bi-Objective Influence Maximization with Consideration of Node Burden

Maximizing Contrasting Opinions in Signed Social Networks

Influence Maximization in Signed Social Networks With Opinion Formation

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