Over the recent decade, much research has been conducted in the field of social networks.
The structure of these networks has been irregular, complex, and dynamic, and certain …

Online social networks have gradually changed the way that people exchange information
as increasingly more people spread information via social networks. Most of the prior …

Influence Maximization (IM) is a classical combinatorial optimization problem, which can be
widely used in mobile networks, social computing, and recommendation systems. It aims at …

With the rapid development of online social networks, the problem of influence maximization
(IM) has attracted much attention from researchers and has been applied in many areas …

Influence maximization aims to select a small set of k influential nodes to maximize the
spread of influence. It is still an open research topic to develop effective and efficient …

The users largely contributing towards product adoption or information utilization in social
networks are identified by the process of influence maximization. The exponential growth in …

Influence maximization (IM) problem, a fundamental algorithmic problem, is the problem of
selecting a set of k users (refer as seed set) from a social network to maximize the expected …

Influence maximization problem is to find a subset of nodes that can make the spread of
influence maximization in a social network. In this work, we present an efficient influence …

This book is written for computer engineers, scientists, and students studying/working in
reinforcement learning and artificial intelligence domains. The book collects recent …

Community structure is one of the common and fundamental characteristics of many real-
world networks such as information and social networks. The structure, function, evolution …