Prediction of links/ connections in social networks is useful for increasing business in the area of telecom and social media. All types of social networking organization are trying to increase their nodes, i.e. the number of members. So the calculation of link prediction is the most important task because this calculation will help how to increase the number of user on a social network. A new technique of link prediction, namely RSM index, is introduced in this paper. Few essential properties have been established. Also, a comparative study with the existing methods is depicted with suitable tables and graphs.
RosenfeldA., Fuzzy graph,
ZadehL.A., FuK.S., ShimuraM. (Eds.), Fuzzy Sets and Their Alications, Academic Press, New York, 1975, pp. 77–95.
2.
KauffmanA., Introduction a la Theorie des Sous-emsembles Flous, Paris: Masson et Cie Editeurs, 1973.
3.
KoczyL.T., Fuzzy graphs in the evaluation and optimization of networks, Fuzzy Sets and Systems46 (1992), 307–319.
4.
SorensenT., A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on Danish commons, Biol Skr5 (1948), 1.
5.
LeichtE.A., HolmeP. and NewmanM.E.J., Vertex Similarity in networks, Phys Rev E73 (2006), 026120.
6.
RavaszE., SomeraA.L., MongruD.A., OltvaiZ.N. and BarabasiA.-L., Hierarchical organization of modularity in metabolic networks, Science297 (2002), 1551.
7.
HuangZ., LiX. and ChenH., Link prediction approach to collaborative filtering, Proceeding of the 5th ACM/IEEE-CS Joint Conference on Digital Libraries, ACM Press, New York, 2005.
8.
LinD., An information-theoretic definition of similarity, Proceeding of the 15th International Conference on Machine Learning, Morgan Kaufman Publishers, San Francisco, 1998.
9.
JaccardP., Etude comparative de la distribution florale dans une portion dse Alprs et des Jura, Bull Soc Vaud Sci Nat37 (1901), 547.
10.
ZhouT., LuL. and ZhangY.-C., Predicting missing links via local information, Eur Phys J B71 (2009), 623.
11.
NowellL. and KleinbergJ., The link prediction problem for social networks, In: CIKM, 2003.
12.
AdamicL.A. and AdarE., Friends and neighbours on web, J Social Networks25 (2003), 211–230.
13.
SamantaS. and PalM., Link prediction in social networks, IGI Global, Chapter 10, 2016.
14.
DasK., SamantaS. and PalM., Study on centrality measures in social networks: A survey, Social Network Analysis and Mining (2018). doi: 10.1007/s13278-018-0493-2
15.
SaltonG. and McGillM.J., Introduction to Modern Information Retrieval, McGraw-Hill, Auckland, 1983.
16.
BarabasiA.L. and AlbertR., Emergence of scaling in random networks, Science286 (1999), 509.
17.
WuJ., ShenJ., ZhouB., ZhangX. and HuangB., General Link Prediction with Influential Node Identification, Physica A: Statistical Mechanics and its Applications Science523, 2019, pp. 996–1007.
18.
WangJ., Ming ZhangQ., and ZhouT., Tag-aware link prediction algorithm in complex networks, Physica A: Statistical Mechanics and its Applications Science523 (2019), 105–111.
19.
BastaniS., Khalili JafarabadA., and Hossein Fazel ZarandiM., Fuzzy models for link prediction in social networks, International Journal of Inteligent System28 (2013), 768–786.
20.
Manh TuanT., Minh ChuanP., AliM., Thi NganT., MittalM. and Hoang SonL., Fuzzy and neutrosophic modeling for link prediction in social networks, Evolving Systems (2018). https://doi.org/10.1007/s12530-018-9251-y.
21.
HanJ. and KamberM., Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers, Bibliographic Notes for Chapter 5, 2006.
22.
LiuJ. and LuL., Link prediction based on local random walk, EPL (Europhysics Letters)89(5) (2010), 1–6.
23.
PatelH.J., PrajapatiR., PanchalM.Prof. and PatelM.J.Dr., A Survey of Graph Pattern Mining Algorithm and Techniques vol. 2, Issue 1, ISSN 2319–4847, 2013.
24.
MaC., ZhouT. and ZhangH., Playing the role of weak clique property in link prediction: A friend recommendation model, Sciencetefic Reports5 (2016), 1–6.
