A reliability-based approach for influence maximization using the evidence theory
This work addresses the influence maximization problem for marketing and viral campaigns in social networks, presenting an incremental improvement by incorporating reliability into influence measures.
The paper tackled the problem of identifying influential users in social networks for maximizing information spread by proposing a reliability-based influence measure using belief functions and a distance-based reliability estimation process. The experiments on a Twitter dataset demonstrated the solution's performance in detecting high-quality social influencers.
The influence maximization is the problem of finding a set of social network users, called influencers, that can trigger a large cascade of propagation. Influencers are very beneficial to make a marketing campaign goes viral through social networks for example. In this paper, we propose an influence measure that combines many influence indicators. Besides, we consider the reliability of each influence indicator and we present a distance-based process that allows to estimate the reliability of each indicator. The proposed measure is defined under the framework of the theory of belief functions. Furthermore, the reliability-based influence measure is used with an influence maximization model to select a set of users that are able to maximize the influence in the network. Finally, we present a set of experiments on a dataset collected from Twitter. These experiments show the performance of the proposed solution in detecting social influencers with good quality.