Community detection in networks is one of the most popular topics of modern network
science. Communities, or clusters, are usually groups of vertices having higher probability of …

In recent years, deep artificial neural networks (including recurrent ones) have won
numerous contests in pattern recognition and machine learning. This historical survey …

In many statistical applications that concern mathematical psychologists, the concept of
Fisher information plays an important role. In this tutorial we clarify the concept of Fisher …

The modern science of networks has brought significant advances to our understanding of
complex systems. One of the most relevant features of graphs representing real systems is …

Uncovering the community structure exhibited by real networks is a crucial step toward an
understanding of complex systems that goes beyond the local organization of their …

To comprehend the multipartite organization of large-scale biological and social systems,
we introduce an information theoretic approach that reveals community structure in weighted …

Ming Li Paul Vitányi Fourth Edition Page 1 An Introduction to Kolmogorov Complexity and Its
Applications Ming Li Paul Vitányi Fourth Edition Texts in Computer Science Page 2 Texts in …

According to the principle of parsimony, model selection methods should value both
descriptive accuracy and simplicity. Here we focus primarily on Bayes factors and minimum …

Many real-world networks are so large that we must simplify their structure before we can
extract useful information about the systems they represent. As the tools for doing these …

In the field of psychology, the practice of p value null-hypothesis testing is as widespread as
ever. Despite this popularity, or perhaps because of it, most psychologists are not aware of …