Link Prediction in Dynamic Networks of Services Emerging during Deployment and Execution of Web Services

Author(s):  
Adam Grzech ◽  
Krzysztof Juszczyszyn ◽  
Paweł Stelmach ◽  
Łukasz Falas
Author(s):  
Jiachen Liu ◽  
Yinan Jiang ◽  
Yashen Wang ◽  
Haiyong Xie ◽  
Jie Ni

Author(s):  
Shashi Prakash Tripathi ◽  
Rahul Kumar Yadav ◽  
Abhay Kumar Rai

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 35937-35945 ◽  
Author(s):  
Carter Chiu ◽  
Justin Zhan

2014 ◽  
Vol 8 (2) ◽  
pp. 2022-2065 ◽  
Author(s):  
Purnamrita Sarkar ◽  
Deepayan Chakrabarti ◽  
Michael Jordan

Author(s):  
Praveen Kumar Bhanodia ◽  
Kamal Kumar Sethi ◽  
Aditya Khamparia ◽  
Babita Pandey ◽  
Shaligram Prajapat

Link prediction in social network has gained momentum with the inception of machine learning. The social networks are evolving into smart dynamic networks possessing various relevant information about the user. The relationship between users can be approximated by evaluation of similarity between the users. Online social network (OSN) refers to the formulation of association (relationship/links) between users known as nodes. Evolution of OSNs such as Facebook, Twitter, Hi-Fi, LinkedIn has provided a momentum to the growth of such social networks, whereby millions of users are joining it. The online social network evolution has motivated scientists and researchers to analyze the data and information of OSN in order to recommend the future friends. Link prediction is a problem instance of such recommendation systems. Link prediction is basically a phenomenon through which potential links between nodes are identified on a network over the period of time. In this chapter, the authors describe the similarity metrics that further would be instrumental in recognition of future links between nodes.


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