Efficient algorithms based on centrality measures for identification of top-K influential users in social networks

Alshahrani, Mohammed; Zhu, Fang; Sameh, Ahmed; Mekouar, Soufiana; Huang, Sheng

doi:10.1016/j.ins.2020.03.060

Cited by 65 publications

(16 citation statements)

References 15 publications

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“…Besides, they do not consider the interaction data between users and resource, which is the sustainable driving force of the resource sharing community. Compared with the existing value evaluation methods which are separated from user evaluation and resource evaluation [28], [29], the advantage of this resource-user pricing model is to establish a bipartite network between users and resources, divide user value into knowledge level and evaluation authority, consider user scoring weight and punish group cheating.…”

Section: Discussionmentioning

confidence: 99%

“…The contribution of this work is threefold. First, different from the traditional method of pricing mechanism of users and resources separately [28], [29], this paper fully considers the interaction between users and resources, and accurately determines the values of both users and resources. Second, different from the current resource sharing community where the weight of users is equal, this paper balances the interests of all users based on the multi-agent game theory, and endows users with different weights that are assigned by the objectivity of pricing and sharing contribution.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Consumption Pricing Mechanism of Scientific and Technological Resources Based on Multi-Agent Game Theory: An Interactive Analytical Model and Experimental Validation

Zheng

et al. 2021

IEICE Trans. Inf. & Syst.

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In the context of Web 2.0, the interaction between users and resources is more and more frequent in the process of resource sharing and consumption. However, the current research on resource pricing mainly focuses on the attributes of the resource itself, and does not weigh the interests of the resource sharing participants. In order to deal with these problems, the pricing mechanism of resource-user interaction evaluation based on multi-agent game theory is established in this paper. Moreover, the user similarity, the evaluation bias based on link analysis and punishment of academic group cheating are also included in the model. Based on the data of 181 scholars and 509 articles from the Wanfang database, this paper conducts 5483 pricing experiments for 13 months, and the results show that this model is more effective than other pricing models -the pricing accuracy of resource resources is 94.2%, and the accuracy of user value evaluation is 96.4%. Besides, this model can intuitively show the relationship within users and within resources. The case study also exhibits that the user's knowledge level is not positively correlated with his or her authority. Discovering and punishing academic group cheating is conducive to objectively evaluating researchers and resources. The pricing mechanism of scientific and technological resources and the users proposed in this paper is the premise of fair trade of scientific and technological resources.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Consumption Pricing Mechanism of Scientific and Technological Resources Based on Multi-Agent Game Theory: An Interactive Analytical Model and Experimental Validation

Zheng

et al. 2021

IEICE Trans. Inf. & Syst.

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“…In (Salehi and Masoumi 2020), Salehi and Masoumi optimize the influence maximization problem in the social networks to identify the influential nodes using the Katz centrality with the biogeographybased optimization method. In (Alshahrani et al 2020) Alshahrani et al optimize the influence maximization problem by obtaining the top-K influential nodes using the Katz centrality as a global centrality and degree centrality as a local measure.…”

Section: Katz Centralitymentioning

confidence: 99%

Centrality Measures in Finding Influential Nodes for the Big-Data Network

Gopalakrishnan

Sridharan

Swaminathan

2021

Handbook of Smart Materials, Technologies, and Devices

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Network analysis determines node-to-node connectivity and examines each node's features. Various tools are available in the literature to study networks like graph theoretical, statistical, and many. As the graph plays a vital role in representing networks, the centrality measurements play an essential role in network analysis. The centrality measures use various metrics for evaluating how important the node is. The centrality predicts the characteristics and importance of the nodes in the network. In this review, selected 16 centrality measures presented along with their implementations.

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“…With this, both the accuracy and time were said to be improved. Two new effective algorithms based on centrality measure and local measure were proposed in [10] under the Independence Cascade (IC) and Linear Threshold (LT) models. With this, the time complexity involved in influential node analysis was found to be reduced.…”

Section: Related Workmentioning

confidence: 99%

Trilateral Spearman Katz Centrality Based Least Angle Regression for Influential Node Tracing in Social Network

Vk¹,

Balasubramanian²

2021

Wireless Pers Commun

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With the epidemic growth of online social networks (OSNs), a large scale research on information dissemination in OSNs has been made an appearance in contemporary years. One of the essential researches is influence maximization (IM). Most research adopts community structure, greedy stage, and centrality measures, to identify the influence node set. However, the time consumed in analyzing the influence node set for edge server placement, service migration and service recommendation is ignored in terms of propagation delay. Considering the above analysis, we concentrate on the issue of time-sensitive influence maximization and maximize the targeted influence spread. To solve the problem, we propose a method called, Trilateral Spearman Katz Centralitybased Least Angle Regression (TSKC-LAR) for influential node tracing in social network is proposed. Besides, two algorithms are used in our work to find the influential node in social network with maximum influence spread and minimal time, namely Trilateral Statistical Node Extraction algorithm and Katz Centrality Least Angle Influence Node Tracing algorithm, respectively. Extensive experiments on The Telecom dataset demonstrate the efficiency and influence performance of the proposed algorithms on evaluation metrics, namely, sensitivity, specificity, accuracy, time and influence spread

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Efficient algorithms based on centrality measures for identification of top-K influential users in social networks

Cited by 65 publications

References 15 publications

Consumption Pricing Mechanism of Scientific and Technological Resources Based on Multi-Agent Game Theory: An Interactive Analytical Model and Experimental Validation

Consumption Pricing Mechanism of Scientific and Technological Resources Based on Multi-Agent Game Theory: An Interactive Analytical Model and Experimental Validation

Centrality Measures in Finding Influential Nodes for the Big-Data Network

Trilateral Spearman Katz Centrality Based Least Angle Regression for Influential Node Tracing in Social Network

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