ABEM: An Adaptive Agent-based Evolutionary Approach for Mining Influencers in Online Social Networks
This addresses influence maximization for online social networks, offering an incremental improvement over existing methods.
The authors tackled the problem of identifying influencers in online social networks, proposing an adaptive agent-based evolutionary approach that outperforms state-of-the-art seeding algorithms in maximizing influence.
A key step in influence maximization in online social networks is the identification of a small number of users, known as influencers, who are able to spread influence quickly and widely to other users. The evolving nature of the topological structure of these networks makes it difficult to locate and identify these influencers. In this paper, we propose an adaptive agent-based evolutionary approach to address this problem in the context of both static and dynamic networks. This approach is shown to be able to adapt the solution as the network evolves. It is also applicable to large-scale networks due to its distributed framework. Evaluation of our approach is performed by using both synthetic networks and real-world datasets. Experimental results demonstrate that the proposed approach outperforms state-of-the-art seeding algorithms in terms of maximizing influence.