Department of Computer Science

Complex Systems

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, Aalto University

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

Characterization of human T cell receptor repertoire data in eight thymus samples and four related blood samples

Nelli Heikkilä, Iivari Kleino, Reetta Vanhanen, Dawit A. Yohannes, Ilkka P. Mattila, Jari Saramäki, T. Petteri Arstila 2021 Data in Brief

Circadian rhythms in temporal-network connectivity

T. Alakorkko, J. Saramaki 2020 CHAOS

Cumulative effects of triadic closure and homophily in social networks

Aili Asikainen, Gerardo Iñiguez, Javier Ureña-Carrión, Kimmo Kaski, Mikko Kivelä 2020 Science Advances

Identifying the inheritable component of human thymic T cell repertoire generation in monozygous twins

Nelli Heikkila, Reetta Vanhanen, Dawit A. Yohannes, Paivi Saavalainen, Seppo Meri, T. Sakari Jokiranta, Hanna Jarva, Ilkka P. Mattila, David Hamm, Silja Sormunen, Jari Saramaki, T. Petteri Arstila 2020 EUROPEAN JOURNAL OF IMMUNOLOGY

Human thymic T cell repertoire is imprinted with strong convergence to shared sequences

Nelli Heikkilä, Reetta Vanhanen, Dawit A. Yohannes, Iivari Kleino, Ilkka P. Mattila, Jari Saramäki, T. Petteri Arstila 2020 MOLECULAR IMMUNOLOGY

Modeling temporal networks with bursty activity patterns of nodes and links

Takayuki Hiraoka, Naoki Masuda, Aming Li, Hang-Hyun Jo 2020 PHYSICAL REVIEW RESEARCH

Burst-tree decomposition of time series reveals the structure of temporal correlations

Hang Hyun Jo, Takayuki Hiraoka, Mikko Kivelä 2020 Scientific Reports

Maximum likelihood estimation for randomized shortest paths with trajectory data

Ilkka Kivimäki, Bram Van Moorter, Manuela Panzacchi, Jari Saramäki, Marco Saerens 2020 Journal of Complex Networks

Waiting-Time Paradox in 1922

Naoki Masuda, Takayuki Hiraoka 2020 Northeast Journal of Complex Systems

Lessons from Ex Post Evaluation of Emerging Mobility Service: Case Kutsuplus

Milos Mladenovic, Nils Haglund, Rainer Kujala, Christoffer Weckström, Jari Saramäki 2020
More information on our research in the Research database.
Research database


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.

Department of Computer Science
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