17. Emphasizing on Space Complexity in Enterprise Social Networks for the Investigation of Link Prediction Using Hybrid Approach

This Social Network Analysis (SNA) has risen as a key strategy which has also gained a significant influence in several domains like healthcare, anthropology, social psychology, and sociolinguistics. Link prediction is a basic computational problem that has recently fascinated the attention of many researchers as an effective technique to be used in SNA in order to know about associations between nodes in any social communities. In link prediction, there is real urge to reduce its size, since the social network data is massive. This chapter aims at reducing the space complexity with respect to dimensionality reduction using soft set theory. Further a friend link algorithm is used to find the node similarities between the social entities, by traversing the entire path of limited length, with the support of “algorithmic small world hypothesis.” The experiments on UCI network data repository show that this approach can reduce the space complexity for forecasting the links that will occur in future. The proposed approach significantly improves the performance of link prediction in social networks.