Department of Computer Science

Complex Systems research group

Complex systems are found at all scales in nature, from the complex machinery operating inside our cells to the human brain, from human sociality to the networked social organization.

People

Complex systems research group people

Publications

Publications from research group Complex Systems

Complex Systems research group

Complex systems are found at all scales in nature, from the complex machinery operating inside our cells to the human brain, from human sociality to the networked social organization.

Complex Systems is a transdisciplinary research area that builds on statistical physics, computer science, data science, and applied mathematics. Complex systems consist of large numbers of interacting elements, with stochastic interactions and non-trivial interaction structure. They are often outcomes of evolutionary processes, and display rich structures and dynamical phenomena from self-organization to phase transitions.

Complex systems are found at all scales in Nature, from the complex machinery operating inside our cells to the human brain and to various aspects of human sociality and the networked social organization of humans. Intriguingly, these systems are often shaped by forces of similar nature, and therefore understanding one system may provide surprising insights into entirely different domains.

Our faculty has a strong focus on complex networks and network science, with applications in (among others) computational social science, network neuroscience, biomedical sciences and health technology, as well as humanities.

Research

Circadian rhythms during a week, graph from research publication

Circadian rhythms

e are currently working on both massive databases of auto-recorded digital data as well as personal sensors (wristbands, bed sensors) to study circadian patterns.

Department of Computer Science
Complex Systems research figure from a publication, Department of Computer Science

Community detection in complex networks

Most networks are not homogenious, meaning that some sets of nodes are more connected among themselves than to the rest of the network. The aim of community detection is to study this mezo-scale structure and devise algorithms that would identify these sets of nodes.

Department of Computer Science
Complex Systems, Aalto University

Public transport networks

In our research project we aim to collect public transport timetable data around the world, curate the data, and publish it in a variety of formats available to be used for both transit planners as well as network scientists.

Department of Computer Science
Department of Computer Science
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