CFIN: A community-based algorithm for finding influential nodes in complex social networks

Khomami, Mohammad Mehdi Daliri; Rezvanian, Alireza; Meybodi, Mohammad Reza; Bagheri, Alireza

doi:10.1007/s11227-020-03355-2

Cited by 27 publications

(28 citation statements)

References 70 publications

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“…Influence Maximization Algorithm (CBIMA) [1] and Community based algorithm for Finding Influential Nodes (CFIN) in complex social networks [2] are implemented in Java.…”

Section: Experimental Evaluation Of Proposed Trilateral Spearman Katz Centrality-based Least Angle Regression (Tskc-lar) Methods and Exismentioning

confidence: 99%

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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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“…Influence Maximization Algorithm (CBIMA) [1] and Community based algorithm for Finding Influential Nodes (CFIN) in complex social networks [2] are implemented in Java.…”

Section: Experimental Evaluation Of Proposed Trilateral Spearman Katz Centrality-based Least Angle Regression (Tskc-lar) Methods and Exismentioning

confidence: 99%

“…Community Finding Influential Node (CFIN) was designed in [2] was designed with the purpose of selecting optimal users on the basis of the community structure, which in turn maximized the influence spread in the networks. The CFIN was split into two stages.…”

Section: Introductionmentioning

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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“…In other words, a high sensitivity value means a test correctly classifies a sample. Table 5 given below shows the sensitivity rate of the proposed TSKC-LAR and existing methods, CBIMA [1] and CFIN [2]. Figure 5 given above shows the sensitivity measure for tracing the influential node in social network.…”

Section: Case 1: Sensitivitymentioning

confidence: 99%

“…A test with a high specificity value means that it is correctly classifies the nodes with the condition more often than a test with a lower specificity. Table 6 given below shows the specificity rate of the proposed TSKC-LAR and existing methods, CBIMA [1] and CFIN [2]. Figure 6 given above illustrates the specificity rate for different numbers of nodes for accessing the internet through base station.…”

Section: Case 2: Specificitymentioning

confidence: 99%

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

Vk¹

2021

Preprint

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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 Centrality-based 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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An Introduction to Learning Automata and Optimization

Kordestani

Mirsaleh

Rezvanian

et al. 2021

Intelligent Systems Reference Library

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CFIN: A community-based algorithm for finding influential nodes in complex social networks

Cited by 27 publications

References 70 publications

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

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

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

An Introduction to Learning Automata and Optimization

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