Authors:
Rohini A,Sudalai Muthu T,DOI NO:
https://doi.org/10.26782/jmcms.2020.03.00012Keywords:
Accuracy of links,Edge weight,Centrality of the network,Proximity of nodes.,Abstract
The social network analysis graph theory concept consists of Vertices, (who may be persons or organization) and Edges (relationship of vertices) one to one or one to many relationships between them. In this paper, we computed the betweenness centrality of the relationship between nodes in the spatial network. The betweenness centrality is an accumulation of solving the shortest path of the nodes, practical implications have validated the range of networks. The prediction of the symbiosis links of the nodes is to be, to consider the strength of the connectivity between a pair of nodes. A weight-based centrality of links is proposed to determine the strong ties in the pair of nodes. The connectivity of link values is used to predict the binding of ties in the network. It allows a value target based purely on the number of links held by each vertex. A Face book data set have been used for the analyzing, the experimental results are drawn. It gives the proposed weight-based algorithm that can yield 98.9% accuracy in finding the strength of the ties in the given network.Refference:
I. A. Zhiyuli, X. Liang, Y. Chen and X. Du, “Modeling Large-Scale Dynamic Social Networks via Node Embedding’s”, IEEE Transactions on Knowledge and Data Engineering, Vol.: 31, No.: 10, PP. 1994-2007, 1. Oct. 2019.
II. C., Zhang, C., Han, X., Ji, Y., AWM: L: adaptive weighted margin learning for knowledge graph embedding, Journal of Intelligent Information Systems, Vol.:53, 2019.
III. Fan, T., Xiong, S., Zhao, W., Yu, T., Information spread link prediction through multi-layer of social network based on trusted central nodes, Peer-to-Peer Networking and Applications, Vol.: 12, 2019.
IV. Guangfu Chen Xu; Jingyi Wang, Jianwen Feng, and Feng, J.” Graph regularization weighed non-negative matrix factorization for link prediction in weighted complex network”, Neurocomputing, Vol.:369, 2018.
V. J. Lin., “Multi-Path Relationship Preserved Social Network Embedding”, IEEE Access, Vol. 7, PP. 26507-26518, 2019.
VI. Kuo Chi Guisshng Yin, Yuxin Dong, Hongbin Dong, “Link Prediction in Dynamic Networks based on the attraction force between nodes”, Knowledge-based System, 2018.
VII. M. Lu, X. Wei, D. Ye, and Y. Dai, “A Unified Link Prediction Framework for Predicting Arbitrary Relations in Heterogeneous Academic Networks”. IEEE Access, Vol.: 7, pp. 124967-124987, 2019.
VIII. Rohini. A, SudalaiMuthu T, “A Weight based Approach for Improving the Accuracy of Relationship in Social Network”, Jour of Adv. Res. In Dynamic & Control Systems, Issue:8,Vol.: 11, 2019.
IX. Rohini. A, SudalaiMuthu T, “A Weight based Scheme for Improving the Accuracy of Relationship in Social Network”, International Journal of Innovative Technology and Exploring Engineering. Issue:11, Vol. 8, 2019.
X. SudalaiMuthu T, Rohini A, “A Correlative Scrutiny for Improving the Career Guidance Links in Social Network”, “International Journal of Engineering and Advanced Technology”, ISSN:2249-8958, Vol.:9, Issue:1, 2019.
XI. X. Li, G. Xu, W. Lian, H. Xian, L. Jiao and Y. Huang, “Multi-Layer Network Local Community Detection Based on Influence Relation”, IEEE Access, Vol.: 7, PP. 89051-89062, 2019.
XII. Zhiyuli, A., Liang, X., Chen, Y., “Highly scalable node embedding for link prediction in very large-scale social networks, World Wide Web, Vol.: 22, 2019.