How does the Web look? How could we tell an abnormal social network from a normal one?
These and similar questions are important in many fields where the data can intuitively be …

The study of social, economic and biological systems is often (when not always) limited by
the partial information about the structure of the underlying networks. An example of …

In most natural and engineered systems, a set of entities interact with each other in
complicated patterns that can encompass multiple types of relationships, change in time and …

Complex networks are key to describing the connected nature of the society that we live in.
This book, the second of two volumes, describes the local structure of random graph models …

We present a quantitative methodology for analyzing the potential for contagion and
systemic risk in a network of interlinked financial institutions, using a metric for the systemic …

We consider sequences of graphs (Gn) and define various notions of convergence related to
these sequences:“left convergence” defined in terms of the densities of homomorphisms …

Recently there has been much interest in studying large-scale real-world networks and
attempting to model their properties using random graphs. Although the study of real-world …

Over the past years, there has been a resurgence of Datalog-based systems in the database
community as well as in industry. In this context, it has been recognized that to handle the …

Background The development of algorithms to infer the structure of gene regulatory
networks based on expression data is an important subject in bioinformatics research …

Discovering causal relations from observational data becomes possible with additional
assumptions such as considering the functional relations to be constrained as nonlinear with …