In most traditional machine learning algorithms, the training and testing datasets have
identical distributions and feature spaces. However, these assumptions have not held in …

In recent years, with the rapid development of online social networks, an enormous amount
of information has been generated and diffused by human interactions through online social …

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 …

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

Clustering of data points has been a profound research avenue in the history of machine
learning algorithms. Using learning automata which are autonomous decision making …

Butterfly Optimization Algorithm (BOA) is a novel meta-heuristic algorithm inspired by the
food foraging behavior of the butterflies. The performance of BOA critically depends upon …

Link prediction is a main social network challenge that uses the network structure to predict
future links. The common link prediction approaches to predict hidden links use a static …

Learning automaton (LA) as one of artificial intelligence techniques is a stochastic model
operating in the framework of the reinforcement learning. LA has been found to be a useful …

Vertex coloring problem is a combinatorial optimization problem in graph theory in which a
color is assigned to each vertex of graph such that no two adjacent vertices have the same …

Learning automata (LA), a powerful tool for reinforcement learning in the field of machine
learning, could explore its optimal state by continuously interacting with an external …