An Improved PageRank Algorithm for Multilayer Networks

Cheriyan, Jo; Sajeev, G P

doi:10.1109/conecct50063.2020.9198566

Cited by 11 publications

(4 citation statements)

References 31 publications

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“…where ∆T is change in information diffusion, c is a centrality constant and m p is m-PageRank that shows the energy distribution capability [18], [19]. Since the node is activated, it transmits the information to its neighbors.…”

Section: B Problem Statementmentioning

confidence: 99%

Influence Minimization With Node Surveillance in Online Social Networks

Cheriyan

Nair

2022

IEEE Access

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Diffusion dynamics is the transfer of information from one node to another. Information diffusion has two goals: maximize or minimize information spread over the network. Attractive information such as innovations, awareness campaigns, branding, and advertising help people positively. However, awful information such as rumors, malicious viruses, pornography, and revenge disturb people. The negative information contributes to chaos; therefore, it must be blocked and inhibited from further diffusion. We are motivated to study the problem, namely Influence Minimization. The new information alters the energy level or entropy associated with a node. Entropy quantifies the influence propagation rate across the network. This article proposes two reduction policies that reduce repulsive information's influence through entropy. We validate the proposed system by considering the user response and surveillance on real-time networks.

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Section: B Problem Statementmentioning

confidence: 99%

Influence Minimization With Node Surveillance in Online Social Networks

Cheriyan

Nair

2022

IEEE Access

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“…In (Ahsan et al 2020), Ahsan et al use the PageRank centrality to predict the elements in the neurosurgically eloquent. Cheriyan et al (Cheriyan and Sajeev 2020) use the PageRank centrality measure for the multilayer complex networks. Here, multilayer complex networks consist of relations between nodes across the layers so that the normal ranking is not efficient in the multilayer network.…”

Section: Pagerankmentioning

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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“…The difference in energy level measure in terms of entropy and it is computed by the equation, ∆E = cm p ∆T . Where ∆T is change in heat energy, c is constant and m p is m-PageRank that shows the energy distribution capability [9]. Since the node is activated, it transmits the information to its neighbours.…”

Section: Problem Statementmentioning

confidence: 99%

Exploring Intelligent Approach of Influence Minimization Considering the Node Surveillance in Online Social Networks

Cheriyan

Sajeev

2021

Preprint

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Attractive information such as innovations, awareness campaigns, branding, and advertising help people positively. Whereas, awful information such as rumors, malicious viruses, pornography, and revenge disturb people. The negative information contributes to chaos among people; therefore, it is to be blocked and hinder from further diffusion. This has motivated us towards the study of the problem named influence minimization. As the real world network can be modeled to a multilayer network, we focus our study towards the information diffusion through a multilayer network. Each node assigns a threshold, and its variation affects the rate of influence propagation across the network. In the influence minimization problem, the energy level of each node changes that help to formulate the function that minimizes the influence propagation. By applying two reduction policies, we are able to optimize our objective of minimizing the influence towards repulsive information. In this article, we consider the user response and its surveillance in the network. Repeated experiments on real networks has helped us to validate the proposed methods.

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An Improved PageRank Algorithm for Multilayer Networks

Cited by 11 publications

References 31 publications

Influence Minimization With Node Surveillance in Online Social Networks

Influence Minimization With Node Surveillance in Online Social Networks

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

Exploring Intelligent Approach of Influence Minimization Considering the Node Surveillance in Online Social Networks

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