Department of Computer Science

Complex Systems

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.

Interested in working with us?

Contact [email protected]

Latest publications

Investor trade allocation patterns in stock markets

Kęstutis Baltakys, Juho Kanniainen, Jari Saramäki, Mikko Kivelä 2023 Journal of Economic Behavior and Organization

Estimating inter-regional mobility during disruption: Comparing and combining different data sources

Sara Heydari, Zhiren Huang, Takayuki Hiraoka, Alejandro Ponce de Leon Chavez, Tapio Ala-Nissila, Lasse Leskelä, Mikko Kivelä, Jari Saramäki 2023 Travel Behaviour and Society

Universal patterns in egocentric communication networks

Gerardo Iñiguez, Sara Heydari, János Kertész, Jari Saramäki 2023 Nature Communications

Analysis of thymic generation of shared T-cell receptor α repertoire associated with recognition of tumor antigens shows no preference for neoantigens over wild-type antigens

Joonatan Mattila, Silja Sormunen, Nelli Heikkilä, Ilkka P. Mattila, Jari Saramäki, T. Petteri Arstila 2023 Cancer Medicine

Critical Drift in a Neuro-Inspired Adaptive Network

Silja Sormunen, Thilo Gross, Jari Saramäki 2023 Physical Review Letters

FreeSurfer 7 quality control : Key problem areas and importance of manual corrections

Vesa Vahermaa, Dogu Baran Aydogan, Tuukka Raij, Reetta-Liina Armio, Heikki Laurikainen, Jari Saramäki, Jaana Suvisaari 2023 NeuroImage

Quantifying daily rhythms with non-negative matrix factorization applied to mobile phone data

Talayeh Aledavood, Ilkka Kivimäki, Sune Lehmann, Jari Saramäki 2022 Scientific Reports


Evgeny Burnaev, Dmitry I. Ignatov, Sergei Ivanov, Michael Khachay, Olessia Koltsova, Andrey Kutuzov, Sergei O. Kuznetsov, Natalia Loukachevitch, Amedeo Napoli, Panos M. Pardalos, Jari Saramäki, Andrey Savchenko, Evgenii Tsymbalov, Elena Tutubalina 2022 Recent Trends in Analysis of Images, Social Networks and Texts

Peripheral differentiation patterns of human T cells

Nelli Heikkilä, Iivo Hetemäki, Silja Sormunen, Helena Isoniemi, Eliisa Kekäläinen, Jari Saramäki, T. Petteri Arstila 2022 EUROPEAN JOURNAL OF IMMUNOLOGY

Herd immunity and epidemic size in networks with vaccination homophily

Takayuki Hiraoka, Abbas K. Rizi, Mikko Kivelä, Jari Saramäki 2022 Physical Review E
More information on our research in the Aalto research portal.
Research portal


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


Much of the work done in the Complex Systems Group that involves empirical networks modeling or method development also involves computational problems that cannot be solved with existing software. We often develop novel algorithms and techniques to push the boundaries of analysis of complex systems. In this page the results of that work is shared in hope that these tools will benefit the research community and other people interested in complex networks.

See our GitHub page for software we have published.


Takayuki Hiraoka

Postdoctoral Researcher

Jari Saramäki

Professorship Saramäki J.
Department of Computer Science
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