Graph Neural Networks (GNNs) have recently become increasingly popular due to their
ability to learn complex systems of relations or interactions arising in a broad spectrum of …

Graphs arise naturally in many real-world applications including social networks,
recommender systems, ontologies, biology, and computational finance. Traditionally …

Recent development in the area of social networks has sought attention of the researchers
to crunch and analyse the data and information of the users to retrieve relevant knowledge …

Despite the importance and abundance of temporal knowledge graphs, most of the current
research has been focused on reasoning on static graphs. In this paper, we study the …

Link prediction is a technique to forecast future new or missing relationships between
entities based on the current network information. Graph theory and network science are …

In many real-life applications it is crucial to be able to, given a collection of link states of a
network in a certain time period, accurately predict the link state of the network at a future …

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

Link prediction (LP), as an attempt to predict event-based future connections within a
network, is the main task of social network analysis (SNA). Accordingly, common LP …

The establishment of new collaborations among scientists fertilizes the scientific
environment, fostering novel discoveries. Understanding the dynamics driving the …

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 …