A Weight based Scheme for Improving the Accuracy of Relationship in Social Network

The usage of social media has become unavoidable in the last decade. The social media is highly dynamic in nature and grows rapidly. The community network offers a rich expedient of various data. The detection of communities is based on the frequency in the networks which is usually represented by graphs. The vertices (nodes) are representing the social actor and the edges (links) represent the relation between those actors. The community link detection is as hard as the graph increases up to millions of vertices and edges. The accuracy of link prediction for inferring missing (erased or broken) links is very complex due to the dynamic nature of links. The links are updated from time to time and the new links are established dynamically. As the links are appeared and disappeared dynamically, the accuracy of identifying the edges of the social network graph of the user is complex in nature. Many efforts have been put up in developing link prediction algorithms in the past, but still there is a lacuna in accuracy in predicting inferred / broken links. A weight based link prediction algorithm is proposed to improve the accuracy of the link prediction on inferred / broken links in the social media. In this method, a weight based link analysis is employed to quantify the relative value between two nodes in the community network. The correlation value for relationship is also determined over a period of time using the designed relationship matrix. The relationship value between the nodes is computed by a Euclidian distance approach. The relationship value of each node is determined by the relationship equation using weight values. The proposed approach is experimented in constrained environment for 2 users’ Facebook usages over a period of a year. The accuracy of relationship is used as performance metrics. The results shown that the accuracy is improved 2.35% more than random predictor method