Nino Antulov-Fantulin

is a senior researcher at the Computational Social Science group at ETH Zurich. Where he also works as a lecturer on the following undergraduate and graduate courses: Modeling and Simulating Social Systems in MATLAB (or Python), Machine Learning and Modelling for Social Networks, Complexity and Global System Science. He got his PhD in Computer science, University of Zagreb, Croatia on the topic "Statistical inference algorithms for epidemic processes on complex networks". Results from his PhD were published in Physical Review Letters journal (in the top 5% of all research outputs scored by Altmetric), Information Sciences journal and covered by New Scientist, Popular Science magazine and different online media Pacific Standard, American Physical Society and others. 

His main interests include complexity and data science. In particular dynamical processes on networks, predictive analytics for FinTech (cryptocurrency & blockchain markets), machine learning, social network analysis, network dismantling, Monte-Carlo algorithms. 

Beside ETH Zurich, he worked at the Rudjer Boskovic Institute and the Faculty of Electrical Engineering and Computing in Croatia and as a visiting scientist at the Robert Koch Institute (Berlin) & Courant Institute of Mathematical Sciences (New York). He worked on several EU projects: SoBigData - "Social Mining & Big Data Ecosystem", Multiplex−“Foundational Research on MULTI-level comPLEX networks and systems”, FOC−“Forecasting Financial Crisis” and e-Lico− “An e-Laboratory for Interdisciplinary Collaborative Research in Data Mining and Data-Intensive Science”. He also works as Supervisor & Panel member of PhD Program in Data Science, Scuola Normale Superiore, Pisa.

He acts as reviewer for IEEE, ACM, Nature Communications, Nature Scientific Reports, Applied Network Science and is in the program committee of "International Conference on Complex Networks and Their Applications" and "ECML-PKDD Applied Data Science Track".


Latest Projects & News



Numerous factors affect the spread of coronavirus. To better predict the progression of the epidemic, researchers at ETH Zurich and the University of California, Los Angeles have started a “datathon”. Participants from around the globe are called on to develop models based on publicly accessible data.

See link to ETH news

Press release about our work

The functioning of many socio-technical systems depends on the ability of its subcomponents or nodes to communicate or interact via its connections, but high connectivity may imply problems. By removing or deactivating a specific set of nodes, a network structure can be dismantled into isolated subcomponents, thereby disrupting the (mal)functioning of a system or containing the spread of misinformation or an epidemic. The researchers (Ren, Gleinig, Helbing, Antulov-Fantulin) at the ETH Zurich recently proposed and published insights about Generalized Network Dismantling.  See EU SoBigData blog postETHZ headline newsACM TechNews

Science Animation of SIR spreading simulation​

The animation is giving a short introduction to the efficient and simple framework that maps the SIR dynamics to an ensemble of weighted networks using a Monte Carlo approach. The same framework is used to describe: (i) Markovian and non-Markovian processes, (ii) continuous and discrete time processes, (iii) finite and infinite-time behavior, (iv) mean-field and exact process representation, (v) MCMC and independent realization sampling and has connections to percolation and disordered network theory. ​

Statistical embedding for directed graphs

We propose a novel statistical node embedding of directed graphs, which is based on a global minimization of pairwise relative entropy and graph geodesics in a non-linear way. Each node is encoded with a probability density function over a measurable real n-dimensional space. Furthermore, we analyze the connection to the geometrical properties of such embedding and characterize the curvature of the statistical manifolds. Extensive experiments show that our proposed embedding is better preserving the global geodesic information of graphs, as well as outperforming existing embedding models on directed graphs in a variety of evaluation metrics, in an unsupervised setting.​ See​

Response to the blog post by Petter Holme

In this short report, we respond to the blog post about the Generalized Network Dismantling. See report