FastCover: An Unsupervised Learning Framework for Multi-Hop Influence Maximization in Social Networks. (arXiv:2111.00463v2 [cs.SI] UPDATED)

Finding influential users in social networks is a fundamental problem with
many possible useful applications. Viewing the social network as a graph, the
influence of a set of users can be measured by the number of neighbors located
within a given number of hops in the network, where each hop marks a step of
influence diffusion. In this paper, we reduce the problem of IM to a
budget-constrained d-hop dominating set problem (kdDSP). We propose a unified
machine learning (ML) …

arxiv framework influence learning networks social social networks unsupervised unsupervised learning