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.
In the process of evolution in Start-up Enterprises, its social networks will often occurred Mutations phenomenon because of environmental changes, the changing conditions, the reverse direction, management of error, etc. This paper analyzed the evolution in social network of Start-up Enterprises through the basic concepts of the theory of mutations, definition of mutations in Start-up Enterprises′ social network evolution, and the using of swallow-tail mutation theory; proposed countermeasures of how to make full use of social network to facilitate Start-up Enterprises timely and accurately identify mutations, take measures, reduce the loss which mutations brought.
Understanding the process of social network evolution: Online-offline integrated analysis of social tie formation
