Foundations for Self-Adaptation and Self-Organization

Dr. Markus Esch (FKIE, Germany)

This tutorial will provide an introduction to the methods and abstractions used in the quantitative study of complex structures and collective dynamical processes emerging in networked systems. Targeting at an audience of computer scientists and engineers, we particularly introduce the statistical physics perspective on self-organizing and self-adaptive network structures that is nowadays common in the modeling and analysis of complex systems occurring in biology, society, physics and technology. A particular emphasis will be placed on the evolution of robust and efficient network topologies based on simple, stochastic rules operating at the microscopic level. We further introduce the generating functions framework, which allows analyzing both the resilience and efficiency of network topologies based on a statistical description of connectivity patterns. In addition, the tutorial will cover the description and analysis of dynamical processes evolving on complex networks, thus providing methods to argue about the performance of distributed protocols.

A particular focus of the tutorial is the introduction of basic methods and abstractions which will enable attendees to benefit from the literature on self-organization and self-adaptation phenomena studied in the fields of statistical physics, network science and complex systems. The tutorial does not require prior knowledge in graph theory, network science or statistical physics, except for the most elementary knowledge in discrete math, probability theory and calculus.

Ingo Scholtes is a postdoctoral researcher at the interdisciplinary chair of Systems Design at ETH Zurich. Ingo Scholtes has a background in computer science and is particularly interested in complexity and collective dynamics in information and communication systems. This includes both technical systems like computer networks, distributed protocols, and P2P systems, but also socio-technical systems like collaborative software engineering processes, information systems and online social networks. Markus Esch is a postdoctoral researcher in the field of distributed systems at the Fraunhofer Institute for Communication, Information Processing and Ergonomics (FKIE). His main research interests are in the field of large-scale distributed systems. He is especially focusing on statistically-structured P2P networks and socially-aware distributed communication systems. For this purpose he is studying interdisciplinary approaches incorporating complex systems science and computer science.

**Lecture 1: Introduction to Network Science** [Slides]

**Lecture 2: Topological Properties of Complex Networks** [Slides]

**Lecture 3: Dynamical Processes in Complex Networks** [Slides]

**Lecture 4: Applications in Distributed Systems** [Slides]

**Lecture 5: Temporal Networks** [Slides]

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