Data forwarding: A new VoteRank and Assortativity based approach to improve propagation time in social networks

Abstract

With the rapid development of social networks, studying and analyzing their structures and behaviors has become one of the most important requirements of businesses. Social network analysis can be used for many different purposes such as product ads, market orientation detection, influential members detection, predicting user behaviors, recommender systems improvements, etc. One of the newest research topics in social network analysis is the enhancement of the information propagation performance in different aspects based on application. In this paper, a new method is proposed to improve few metrics such as distribution time and precision on social networks. In this method, the local attributes of nodes and also the structural information of the network is used to forward data across the network and reduce the propagation time. First of all, the centrality and Assortativity are calculated for all nodes separately to select two sets of nodes with the highest values for both criteria. Then, the initial active nodes of the network are selected by calculating the intersection of the two sets. Next, the distribution paths are detected based on the initial active nodes to calculate the propagation time. The performance analysis results show that the proposed method has better outcomes in comparison to other state-of-the-art methods in terms of distribution time, precision, recall, and AUPR criteria.

Keywords

Information propagation time node assortativity VoteRank centrality data forwarding influential nodes detection

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