A Comprehensive Insight towards Research Direction in Information Propagation

S, Selva Kumar; Kayarvizhy, N.

doi:10.14569/ijacsa.2017.080406

Cited by 2 publications

(1 citation statement)

References 60 publications

(60 reference statements)

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“…This section will further brief about the existing studies towards influence maximization. Our previous review paper has presented a discussion about research gap associated with the study of information propagation [10]. Various existing strategies have been evolved that has used linear thresholding mechanism for obtaining influential node in viral marketing [11].…”

Section: A Backgroundmentioning

confidence: 99%

Framework for Computing Potential Node with Higher Influence in Complex Social Network

Kuamr S*,

2020

IJRTE

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The increasing data complexity in social network imposes challenges towards carrying out investigation towards analytics. Similar scenario has started evolving in social network most recently owing to the usage of ubiquitous devices which gives rise to accumulation of complex set of data that offers potential challenge to understand the potential node. Identification of potential node is an essential demand to understand the role player in social network. Therefore, this paper presents a novel analytical methodology to construct a discrete community with higher precision along with a novel progressive algorithm for identifying the potential node that offers a higher degree of influence in social network. Dynamic optimization, as well as probability theory, has been used in order to perform modeling of the proposed system. Along with an effective computational performance, the comparative analysis shows that proposed system offers better performance in contrast to existing techniques with respect to influence degree and information processing time. The inferencing of the quantified outcome status shows that the formulated approach attains ~70% performance improvement in the case of minimizing the processing time as compared to the Chen approach. The identification of potential node in four different types of networks have also significantly improved with negligible computational effort.

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Section: A Backgroundmentioning

confidence: 99%

Framework for Computing Potential Node with Higher Influence in Complex Social Network

Kuamr S*,

2020

IJRTE

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Novel Approach for Optimizing Information Propagation in Dynamic Social Network

Selva

Kayavizhy

2020

Wseas Transactions on Computers

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Identification of potential node is one of the essential operations to be carried out in social network analysis as it is necessary to undertake various important decisions associated with the information propagation. Review of existing literature towards social network highlights that there is very less work carried out towards emphasizing potential node. Therefore, the proposed study offers a novel and unique solution that is capable of optimizing the level of information propagation when it is exposed to dynamic networks. The proposed study has been modeled using graph theory and it uses degree centrality distribution in order to offer more insight towards analyzing the selection of potential nodes in a social network. The study significantly contributes towards precise information propagation and its sustainability in the presence of dynamic social network in every aspect.

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A Comprehensive Insight towards Research Direction in Information Propagation

Cited by 2 publications

References 60 publications

Framework for Computing Potential Node with Higher Influence in Complex Social Network

Framework for Computing Potential Node with Higher Influence in Complex Social Network

Novel Approach for Optimizing Information Propagation in Dynamic Social Network

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