Link prediction is a paradigmatic problem in network science, which aims at estimating the
existence likelihoods of nonobserved links, based on known topology. After a brief …

The illicit wildlife trade is a pervasive and global problem that has far-reaching impacts on
both society and the environment. Aside from threatening numerous species around the …

Deep learning on graphs has become one of the hottest topics in machine learning. The
book consists of four parts to best accommodate our readers with diverse backgrounds and …

Abstract Social Network Analysis (SNA) has been a popular field of research since the early
1990s. Law enforcement agencies have been utilizing it as a tool for intelligence gathering …

As a natural extension of link prediction on graphs, hyperlink prediction aims for the
inference of missing hyperlinks in hypergraphs, where a hyperlink can connect more than …

David Knoke and Song Yang′ s Social Network Analysis, Third Edition provides a concise
introduction to the concepts and tools of social network analysis. The authors convey key …

Real-world applications often combine learning and optimization problems on graphs. For
instance, our objective may be to cluster the graph in order to detect meaningful …

Link prediction exercises may prove particularly challenging with noisy and incomplete
networks, such as criminal networks. Also, the link prediction effectiveness may vary across …

Networks are now more crucial than ever for modeling complex systems with interconnected
components. Many methods have been developed to infer unobserved links or predict latent …

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 …