  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.
