Shortest Path Algorithms for Social Network Strengths

Abstract

In social media directed links can represent anything from close friendship to common interests. Such directed links determine the flow of information and hence indicate an individual influence on others. The influence of a person X over person Y is defined as the ratio of Y’s investment that Y makes on X. Most contemporary networks return source–target paths in an online social network as a result of search ranked by degrees of separation. This approach fails to reflect tie of social strength (i.e., intimacy of two people in terms of interaction), and does not reflect asymmetric nature of social relations (i.e., if a person X invests time or effort in person Y, then the reverse is not necessarily true). In this paper, it is proved that in social graph result can prove to be more effective by incorporating the concept of directed and weighted influence edges taking into account both asymmetry and tie strength. The study is based on two real-world networks: Twitter capturing its retweet data and DBLP capturing its author–coauthor relationship. The experiments have been conducted based on two algorithms—Dijkstra shortest path algorithm and influence-based strongest path algorithm. Then a comparative study was done capturing different cases in which strongest path algorithm was better than shortest path algorithm in different cases.