A Distributed Link Prediction Algorithm Based on Clustering in Dynamic Social Networks

With the rapid development of the Internet, social networks have shown an unprecedented development trend among college students. Closer social activities among college students have led to the emergence of college students with new social characteristics. The traditional method of college students’ group classification can no longer meet the current demand. Therefore, this paper proposes a social network link prediction method-combination algorithm, which combines neighbor information and a random block. By mining the social networks of college students’ group relationships, the classification of college students’ groups can be realized. Firstly, on the basis of complex network theory, the essential relationship of college student groups under a complex network is analyzed. Secondly, a new combination algorithm is proposed by using the simplest linear combination method to combine the proximity link prediction based on neighbor information and the likelihood analysis link prediction based on a random block. Finally, the proposed combination algorithm is verified by using the social data of college students’ networks. Experimental results show that, compared with the traditional link prediction algorithm, the proposed combination algorithm can effectively dig out the group characteristics of social networks and improve the accuracy of college students’ association classification.

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Link Prediction Based on Subgraph Evolution in Dynamic Social Networks

2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing ◽

10.1109/passat/socialcom.2011.15 ◽

2011 ◽

Cited By ~ 25

Author(s):

Krzysztof Juszczyszyn ◽

Katarzyna Musial ◽

Marcin Budka

Keyword(s):

Social Networks ◽

Link Prediction ◽

Dynamic Social Networks

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Link Prediction in Dynamic Social Networks: A Literature Review

2018 IEEE 5th International Congress on Information Science and Technology (CiSt) ◽

10.1109/cist.2018.8596511 ◽

2018 ◽

Cited By ~ 2

Author(s):

Mohammad Marjan ◽

Nazar Zaki ◽

Elfadil A. Mohamed

Keyword(s):

Social Networks ◽

Literature Review ◽

Link Prediction ◽

Dynamic Social Networks

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A Feasible Temporal Links Prediction Framework Combining with Improved Gravity Model

Symmetry ◽

10.3390/sym12010100 ◽

2020 ◽

Vol 12 (1) ◽

pp. 100

Author(s):

Xinyu Huang ◽

Dongming Chen ◽

Tao Ren

Keyword(s):

Social Networks ◽

Gravity Model ◽

Link Prediction ◽

Temporal Networks ◽

Scientific Society ◽

Multidisciplinary Study ◽

Dynamic Social Networks ◽

Traditional Research ◽

Real World Datasets ◽

Social Applications

Social network analysis is a multidisciplinary study covering informatics, mathematics, sociology, management, psychology, etc. Link prediction, as one of the fundamental studies with a variety of applications, has attracted increasing focus from scientific society. Traditional research based on graph theory has made numerous achievements, whereas suffering from incapability of dealing with dynamic behaviors and low predicting accuracy. Aiming at addressing the problem, this paper employs a diagonally symmetrical supra-adjacency matrix to represent the dynamic social networks, and proposes a temporal links prediction framework combining with an improved gravity model. Extensive experiments on several real-world datasets verified the superiority on competitors, which benefits recommending friends in social networks. It is of remarkable significance in revealing the evolutions in temporal networks and promoting considerable commercial interest for social applications.

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