Networks and complex systems

Vorlesung mit Übung (4 SWS)

Introduction

A complex system consists of many components that interact with one another, giving rise to emergent behaviors that are determined by—but not immediately predictable from—relatively simple interaction rules. Examples of complex systems include the global climate, ecosystems, the human brain, epidemic spreading, and large-scale software or electronic systems. Such systems are often modeled as networks of interacting entities, drawing on theory and methods from diverse fields, including graph theory, computer science, statistical physics, and information theory.

This course provides a multidisciplinary introduction to the fundamentals of network analysis and the modeling of complex systems. Topics include elementary graph theory, random network models, network algorithms, characterization of network structure, network robustness, and dynamical processes on networks. The course also covers the statistical analysis of network data, which serves as a paradigmatic example of high-dimensional, interdependent data—a central challenge in modern data science.

Students will be able to represent complex systems as networks and to integrate theoretical models, algorithms, and real-world data in order to draw meaningful conclusions about their structure and behavior. They will be able to identify the implications of different network architectures, and to compare, evaluate, and assess the relevance of random network models for empirical systems. In addition, students will be able to simulate and analyze dynamical processes on networks, extract statistically significant information from empirical network data, and identify and interpret structural patterns.

Parameters

Time: TBD
Location: TBD
Language: English
Evaluation: Final oral exam

Schedule

TBD