Influence maximization is pivotal in network analysis, identifying critical individuals for
optimal information spread. This thesis introduces a novel dynamic non-progressive …

Influence maximization is a hot research topic in the social computing field and has gained
tremendous studies motivated by its wild application scenarios. As the structures of social …

Selecting a set of initial users from a social network in order to maximize the envisaged
number of influenced users is known as influence maximization (IM). Researchers have …

A serious challenge when finding influential actors in real-world social networks is the lack
of knowledge about the structure of the underlying network. Current state-of-the-art methods …

Influence maximization (IM) is formulated as selecting a set of initial users from a social
network to maximize the expected number of influenced users. Researchers have made …

Influence maximization aims to find a subset of seeds that maximize the influence spread
under a given budget. In this paper, we mainly address the data-driven version of this …

Influence maximization is the problem of finding a set of users in a social network, such that
by targeting this set, one maximizes the expected spread of influence in the network. Most of …

Influence diffusion in social networks has been intensively studied over last two decades.
Most prior arts assume that the underlying network structure is static, remaining fixed during …

Social influence maximization problem has been widely studied by the industrial and
theoretical researchers over the years. However, with the skyrocketing scale of networks and …

How could one identify a potential influencer, or how would admissions look like in a
University program for influencers? In the realm of social network analysis, influence …