Independence of Sources in Social Networks
This work addresses the need to better understand user relationships in social networks for tasks like community detection and influencer identification, but it appears incremental as it applies an existing theory to a new context.
The paper tackled the problem of qualifying links between users in social networks by proposing an approach based on belief functions to estimate degrees of cognitive independence, and it was tested on a large dataset from Twitter.
Online social networks are more and more studied. The links between users of a social network are important and have to be well qualified in order to detect communities and find influencers for example. In this paper, we present an approach based on the theory of belief functions to estimate the degrees of cognitive independence between users in a social network. We experiment the proposed method on a large amount of data gathered from the Twitter social network.