Complex Systems
Complex systems are systems composed of many interconnected and interacting components that give rise to collective behaviors and emergent properties not present in individual parts. These systems are found across various domains, including economics and social sciences, where the behavior of the whole cannot be easily predicted by understanding each part individually ("the whole is other than the sum of its parts"). In complex systems, interactions among components often follow nonlinear dynamics, meaning small changes can lead to disproportionately large effects. Examples include financial markets, social media, society, where local interactions between traders or individuals can produce global patterns like market crashes, polarisation, or pandemics. Key characteristics of complex systems include network structure, feedback loops, adaptation, self-organisation, and emergence. Studying complex systems requires interdisciplinary collaboration, including tools from network science, agent-based modeling, and statistical physics, to understand how these systems evolve, adapt, and respond to external forces. The Complex Systems perspective combined to applied disciplines (economics, sociology, criminology) is promising to link microscopic behaviour and (emergent) macroscopic patterns, like the society, the economy, global crisis, wars, among other phenomena.
Network Science
Network science is an interdisciplinary field that studies the structure, dynamics, and behavior of complex systems through the analysis of the structure of networks. These networks consist of nodes (representing entities) and edges (representing relationships or interactions), and can model a wide range of phenomena, from social interactions to technological infrastructures and economic markets. By examining the patterns of connectivity within these networks, network science seeks to uncover underlying principles that govern how systems function, evolve, and respond to changes. Network science exploits tools from mathematics, physics, computer science, and sociology to analyze properties like clustering, robustness, and node centrality, providing insights into how information spreads, how systems can be optimised, and how resilience can be enhanced in interconnected systems. Network science is at the core of CSI research.
Data Analytics
Data analytics is the process of cleaning, transforming, examining, and modeling data to discover useful information, draw conclusions, and support decision-making. By applying various statistical techniques (e.g. network analytics, machine learning, visualisation), data analytics provides insights from structured and unstructured data. It helps to the understanding of patterns, trends, and relationships in data. Data analytics is divided into several categories. At CSI, we use different techniques from descriptive analytics (explaining what happened) to predictive analytics (forecasting future events). Our research involves data collection, the design of novel methods to extract information from data (particularly using networks), and use of existing methods in novel data.
Agent-based Modelling
An agent-based model is a computational framework used to study complex systems by modeling the interactions and evolution of individual entities, or "agents" within an environment. Each agent operates based on a set of rules or behaviors, and through their interactions, collective phenomena and emergent behaviors may arise. Agent-based models focus on bottom-up dynamics, where the system's overall behavior emerges from the local actions and interactions of individual agents. This approach is particularly useful for studying systems where heterogeneous agents (e.g., individuals, firms, countries) make decisions, adapt, and evolve over time. Such models allow researchers to explore how micro-level behaviors contribute to macro-level outcomes, such as market dynamics, social phenomena, infection spread, or ecosystem stability, providing insights into both predictable patterns and unexpected emergent behaviors in complex systems.
Scaling Laws
Scaling laws in social and economic systems describe how various properties of these systems change in proportion to the size of the system, often revealing underlying patterns that remain consistent across different scales. These laws, which are typically expressed as power laws, indicate that certain variables, such as population size, wealth distribution, or city infrastructure, scale predictably with other factors like productivity, innovation, or resource consumption. For example, in urban systems, the number of patents or GDP tends to scale superlinearly with city population, meaning larger cities are disproportionately more productive and innovative than smaller ones. Similarly, scaling laws are observed in social networks, where the number of connections individuals maintain often follows predictable patterns. These laws provide insights into the self-organising nature of complex systems.
Sampling
Network sampling is the process of selecting a subset of nodes and edges from a larger network to create a smaller, representative version of the original system for analysis. Given the complexity and size of many real-world networks (e.g. social media platforms), it is often computationally infeasible or unnecessary to study the entire network. By using various sampling techniques, such as random node selection, snowball sampling, or edge-based sampling, researchers can reduce the network's size while preserving key structural properties. At CSI, we have experience with respondent-driven sampling, which is an unbiased methodology to sample hard-to-reach populations based on hidden social networks.
Data sets
We have exclusive data sets in social, economic and health systems. There are possibilities for collaborative work. Check out our previous publications for more details.
Computers
Access to high-performance computing infrastructure at UGent is available, see
HPC-UGent.