scholarly journals Comparison Analysis of Link Prediction Algorithms in Social Network

2015 ◽  
Vol 111 (16) ◽  
pp. 27-29 ◽  
Author(s):  
Sahil Gupta ◽  
Shalini Pandey ◽  
K.K.Shukla K.K.Shukla
Keyword(s):  
Social Network ◽  
Link Prediction ◽  
Comparison Analysis ◽  
Prediction Algorithms

Efficient incremental dynamic link prediction algorithms in social network

2017 ◽  
Vol 132 ◽  
pp. 226-235 ◽  
Author(s):  
Zhongbao Zhang ◽  
Jian Wen ◽  
Li Sun ◽  
Qiaoyu Deng ◽  
Sen Su ◽  
...  
Keyword(s):  
Social Network ◽  
Link Prediction ◽  
Prediction Algorithms ◽  
Dynamic Link

scholarly journals The Algorithm of Link Prediction on Social Network

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Liyan Dong ◽  
Yongli Li ◽  
Han Yin ◽  
Huang Le ◽  
Mao Rui
Keyword(s):  
Social Network ◽  
Network Topology ◽  
Link Prediction ◽  
Prediction Algorithm ◽  
Similarity Function ◽  
Information Network ◽  
Global Information ◽  
Data Set ◽  
Prediction Algorithms ◽  
Topology Information

At present, most link prediction algorithms are based on the similarity between two entities. Social network topology information is one of the main sources to design the similarity function between entities. But the existing link prediction algorithms do not apply the network topology information sufficiently. For lack of traditional link prediction algorithms, we propose two improved algorithms: CNGF algorithm based on local information and KatzGF algorithm based on global information network. For the defect of the stationary of social network, we also provide the link prediction algorithm based on nodes multiple attributes information. Finally, we verified these algorithms on DBLP data set, and the experimental results show that the performance of the improved algorithm is superior to that of the traditional link prediction algorithm.

scholarly journals Group Relationship Mining of College Students Based on Predictive Social Network

2022 ◽  
Vol 2022 ◽  
pp. 1-9
Author(s):  
Huazhang Liu
Keyword(s):  
College Students ◽  
Social Networks ◽  
Social Network ◽  
Complex Network ◽  
Link Prediction ◽  
Rapid Development ◽  
Combination Method ◽  
Prediction Algorithm ◽  
The Social ◽  
Random Block

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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