Covering formulation, algorithms, and structural results, and linking theory to real-world
applications in controlled sensing (including social learning, adaptive radars and sequential …

We review classic results and recent progress on equilibrium analysis, dynamics, and
optimal interventions in network games with both continuous and discrete strategy sets. We …

Models for the processes by which ideas and influence propagate through a social network
have been studied in a number of domains, including the diffusion of medical and …

Several systems can be modeled as sets of interconnected networks or networks with
multiple types of connections, here generally called multilayer networks. Spreading …

Influence Maximization is an important problem in social networks, and its main goal is to
select some most influential initial nodes (ie, seed nodes) to obtain the maximal influence …

Social media has brought a revolution on how people are consuming news. Beyond the
undoubtedly large number of advantages brought by social-media platforms, a point of …

State-of-the-art classical influence maximization (IM) techniques are" competition-unaware"
as they assume that a group (company) finds seeds (users) in a network independent of …

The spread of rumor or misinformation in social networks may cause bad effects among the
public. Thus, it is necessary to find effective strategies to control the spread of rumor …

Social media and the web have provided a foundation where users can easily access
diverse information from around the world. However, over the years, various factors, such as …

Influence maximization deals with identification of the most influential nodes in a social
network given an influence model. In this paper, a game theoretic framework is developed …