Cyberspace Management

Entry requirements: Basics of social, technical, economical and other networks

Credits: 4

Course: Elective

Language of the course: Russian

Lecturer

Nikolay Butakov

Objectives

Students will learn:

  • To make non-standard decisions related to process management in virtual social networks taking into account the relevance and ethical restrictions
  • Approaches and methods of modelling network processes
  • Basic methods of modelling processes in networks, calibration and validation of models at ultra-large amounts of data
  • Analysis and numerical methods of modelling information dissemination processes, dynamics of network communities, structures and organizations
  • To use basic packages for modelling network processes, to select databases in accordance to model design and research objectives
  • To use methods and software tools to implement mathematical models of network processes and network dynamics

Contents

  • Introduction to modelling of processes in networks: Introduction to modelling, modelling as the third paradigm of scientific knowledge, artificial communities, generative science, aims and objectives of modelling processes in networks.
  • Approaches and methodology of modelling processes in networks: Basic principles, theoretical foundation of modelling network processes, approaches to modelling processes in communication networks.
  • Processes in social networks: Types and forms of representation of processes in virtual networks, network processes, approaches to classification of network processes, evolution of communities, dynamics of egocentric networks, metrics and indicators used to define the dynamics of network processes.
  • Preparation and performance of experiments with the use of computer models: Model development stages, preparation of input data, development of conceptual model of process, evaluation of validity and verification of models, statistical processing of the experiment results, model documentation.
  • Development stages for computer model of network process: Analytical, computational models, epidemiological models of information dissemination, network limits, scope, model formalization methods, computer modelling with the help of computer packages such as Netlogo and AnyLogic, plugin options and development environments based on Python.
  • Cellular automata method: Basic principles of modelling network processes based on cellular automata, properties of cellular automata, types of cellular automata, implementation of cellular automata in modern packages and environments of development and modelling.
  • Agent-based modelling of processes in social networks: Basics of agent-based modelling, advantages and disadvantages of agent-based modelling, field of application of agent-based modelling, formalization of of agent-based models, software tools for modelling network processes based on multi-agent systems.
  • Data for building and evaluating models of network processes: Sources and types of data for building models of network processes, databases, work with open data, forms of data representation, data pre-processing for development, calibration and validation of a model, data operation cycle.

Format

Lectures, practical sessions and labs

Assessment

Attendance is mandatory. Students should complete all the assignments. The final grade is based on the student performance throughout the course.