NetSI Hubs

The Network Science Institute is growing! In 2022, NetSI established two new institute hub sites in London, UK and Portland, Maine.

NetSI is establishing new Institute hub sites in the US and Europe to advance research, education, and innovation centered around network science. In the last year, NetSI opened two new sites: in Portland, Maine at the Roux Institute and at the new Northeastern London campus. These hub sites will serve as pillars of the expansive Northeastern University's global enterprise.

NetSI Global will be dedicated to engaging and integrating regional network science communities and partnering with regional talent, institutions, ecosystems of innovation. With this initiative, we expect that each site will grow into unique NetSI hub communities that will continue to innovate and propel interdisciplinary scientific advancement.

NetSI-London
Our new London hub will facilitate the exchange of knowledge and expertise between the US and Europe and will enable students and scholars to collaborate and connect seamlessly across sites. As collaborations coalesce and emerge, the NetSI-London team will establish new research programs that explore novel methods to understand dynamics, contagion, and collective action in socio-economic ecosystems.

JK Rofling

People

István Z. Kiss
Professor

István Z. Kiss is a Professor in the Network Science Institute at Northeastern University London. His research is at the interface of network science, dynamical systems and stochastic processes, and concerns both theoretical and data-driven problems. Examples include network inference, exactness of mean-field models, temporal and higher-order networks, adaptive/dynamic networks, resilience of power networks and the study of spreading processes in general.

Biography

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István Z. Kiss is a Professor of Applied Mathematics. His research is at the interface of network sciences, dynamical systems and stochastic processes. In particular, he focuses on dynamical processes on static and dynamic networks, using mainly epidemic models. He is working on developing theoretical models that capture complexities arising in real networks, such as heterogeneity in the characteristics, behaviour and interaction of individuals, as well as higher-order network structure. He contributed significantly to: (a) identifying links between approximate models and their rigorous mathematical counterpart, (b) proving the exactness of certain epidemic models on tree-like networks, (c) highlighting linkages between various modern epidemic models, and (d) extending modelling to more realistic networks exhibiting clustering and motifs. He has also worked on applications of network science to areas such as the spread of innovations, livestock diseases, neuronal networks, network inference, spread of COVID19 involving vulnerable people such as single household parents and those receiving domiciliary care, with more recent work focusing on power and shipping networks.

Following a PhD in Applied Mathematics from University of Leeds, Prof Kiss has completed a four year position as a postdoctoral research fellow at the University of Oxford where he used network science to develop data-driven models of the spread of foot and mouth disease and other livestock diseases, such as scrapie and bovine TB.

Prof Kiss has also fulfilled a few important administrative roles at University of Sussex; namely he was the Director of Research and Knowledge in the School of Mathematical and Physical Sciences (Aug 2019 – July 2022) and was heavily involved in an extremely successful REF2021 (Research Excellence Framework 2021, UK) for both Mathematics and Physics Departments. Previously, he was the Impact and Knowledge Exchange Lead in Mathematics and was responsible for identifying and managing the completion of a suite of impact case studies which are an integral part of the REF submission. More recently, he acted as the Director of Research in Mathematics (September 2022 - May 2023). He is also a Fellow of HEA (Higher Education Academy, 2010 - present) and in recognition of the quality of his teaching he has won Teaching Awards at Sussex in 2010, 2014 and 2022.

He has published over 100 papers in peer-reviewed conferences and journals, four book chapters, and a monograph titled “Mathematics of network epidemics: from exact to approximate models” in the Interdisciplinary Applied Mathematics series of Springer (2017).

Featured Publications

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Mathematics of epidemics on networks
Kiss, István Z., Joel C. Miller, and Péter L. Simon.
Cham: Springer 598 (2017): 31.

Exact epidemic models on graphs using graph-automorphism driven lumping
Simon, Péter L., Michael Taylor, and Istvan Z. Kiss.
Journal of mathematical biology 62 (2011): 479-508.

The network of sheep movements within Great Britain: network properties and their implications for infectious disease spread
Kiss, Istvan Z., Darren M. Green, and Rowland R. Kao.
Journal of the Royal Society Interface 3.10 (2006): 669-677.

The impact of information transmission on epidemic outbreaks
Kiss, Istvan Z., et al.
Mathematical biosciences 225.1 (2010): 1-10.

Exact equations for SIR epidemics on tree graphs
Sharkey, Kieran J., et al.  
Bulletin of mathematical biology 77 (2015): 614-645.

Giovanni Petri
Professor

Giovanni Petri is a Professor in the Network Science Institute at Northeastern University London. His research spans the analysis of neuroimaging data and AI systems with topological techniques, the formalization of cognitive control models with tools of statistical mechanics and network theory, and the study of the predictability of socio-technical systems.

Biography

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Giovanni Petri is a Professor at the Network Science Institute, Northeastern University London. With a broad background in theoretical physics and applied mathematics, he has successfully applied his expertise to address various research areas, including network science and higher-order systems, dynamical processes, with a particular focus on neuroimaging, computational neuroscience, and, more recently, artificial intelligence.

Alongside these core topics, his research also encompasses the study of group dynamics in social systems, novel methods for parsimonious feature selection, and information-theoretic limits to predicting complex systems. He has collaborated with researchers from diverse disciplines, including mathematicians, physiologists, psychologists, network neuroscientists, epidemiologists, and computer scientists.

Giovanni Petri has authored over 50 peer-reviewed articles in high-impact interdisciplinary journals such as Science, Nature Physics, Physical Review Letters, Nature Communications, Neuroimage, and the Journal of Neuroscience. Major contributions include a review and a perspective on higher-order systems, the topological functional structure of altered brain states,   a temporal model of simplicial interactions to describe the effects of group interactions in social spreading processes, a graph-theoretic approach to cognitive control to multitasking capacity, and the discovery of the evolutionarily conserved role of oxytocin for social contagion.   He guest-edited a special issue of "Network Neuroscience" dedicated to topological applications in brain networks (in collaboration with Prof. M. Kringelbach and Dr. Paul Expert) and co-edited the book "Higher-order Systems" (with Prof. F. Battiston) for the Springer Nature series "Understanding Complex Systems".  

Giovanni Petri obtained his PhD from Imperial College London in 2012 studying the mathematics of complex networks. He then joined ISI Foundation as a postdoctoral researcher working on the topological structure of complex systems.  Since 2016, Giovanni Petri has led his own lab, initially as a Senior Research Scientist at ISI Foundation and then as a Principal Researcher at CENTAI Institute.

He mentored and supervised 6 postdocs, 2 PhD students, 2 junior researchers, and 13 MSc and BSc students. Additionally, he leads a working group focused on the topology of learning systems, attracting participants from various research labs and institutions. To fund his research, he has secured both large grants, such as the 3-year ADnD project (Compagnia San Paolo) and the 5-year grant from Project CETI, as well as smaller grants targeting specific objectives.

Beyond his research, Giovanni Petri is committed to open reproducible science, scientific outreach, and public engagement. Together with his group and collaborators, he has developed and distributed a set of open-source tools for topological data analysis (TDA) and modeling (Holes, SCM, DyNeuSR, XGI), aiming to lower the entry barrier to TDA and network inference for scientists from non-mathematical or non-computational disciplines.He has organized multiple workshops, satellite meetings, and conferences to bridge disciplinary gaps and foster collaboration. This includes conducting a TDA crash course at the Applied Machine Learning Days 2019, organizing a series of satellite meetings (TopoNets) at both the NetSci conference and Conference on Complex Systems since 2014, and co-organizing the annual CCS Warm Up school, aimed at young researchers in complex systems, which is still ongoing.

In 2018, he served as a co-organizer of "ATMCS7: Conference on Algebraic Topology: Methods, Computation, and Science".
To enhance public awareness and build communities around scientific topics, he initiated initiatives such as Databeers Torino, which brings together individuals interested in data science through engaging talks and networking events. He has actively participated in other outreach initiatives, including Pint for Science."

Featured Publications

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The physics of higher-order interactions in complex systems
Federico Battiston1, Enrico Amico, Alain Barrat, Ginestra Bianconi, Guilherme Ferraz de Arruda, Benedetta Franceschiello, Iacopo Iacopini, Sonia Kéfi, Vito Latora, Yamir Moreno, Micah M. Murray, Tiago P. Peixoto, Francesco Vaccarino and Giovanni Petri
Nature Physics
volume 17, pages1093–1098 (2021)

Topological limits to the parallel processing capability of network architectures
Giovanni Petri, Sebastian Musslick, Biswadip Dey, Kayhan Özcimder, David Turner, Nesreen K. Ahmed, Theodore L. Willke, and Jonathan D. Cohen
Nature Physics
volume 17, pages646–651 (2021)

Evolutionarily conserved role of oxytocin in social fear contagion in zebrafish
Ibukun Akinrinade, Kyriacos Kareklas, Magda C. Teles1, Thais K. Reis, Michael Gliksberg, Giovanni Petri, Gil Levkowitz, Rui F. Oliveira
Science 379, 1232–1237 (2023)

Homological scaffolds of brain functional networks
Petri G, Expert P, Turkheimer F, Carhart-Harris R, Nutt D, Hellyer PJ, Vaccarino F.
J. R. Soc. Interface 11: 20140873. (2014)

Simplicial models of social contagion
Iacopo Iacopini, Giovanni Petri, Alain Barrat, & Vito Latora
Nature Communications
volume 10, Article number: 2485 (2019)

Riccardo Di Clemente
Associate Professor

Riccardo Di Clemente is an Associate Professor in the Network Science Institute at Northeastern University London. Riccardo develops mathematical frameworks to analyze and model the complex social connections that govern human behavior and interactions within cities and online. His research utilizes network theory, complex systems computational social science, and machine learning methods to investigate the digital footprints left by individuals in their daily routines.

Biography

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Riccardo Di Clemente is an Associate Professor at Northeastern University London, Core Faculty at NetSI Northeastern Univesity, Alan Turing Fellow of The Alan Turing Institute, and Visiting Professor at Sony Computer Science Lab Paris.

Riccardo obtained his undergraduate B.Sc. & M.Sc. degrees in physics at Sapienza University of Rome. He received his Ph.D. Europaeus in Economics (at IMTLucca, Italy with visiting Institution INET@Oxford) where he applied Complex Systems tools to Economics and Social system. Afterward, he completed his postdoctoral research at the Institute of Complex Systems in Rome and then at Civil and Environmental Engineering department of MIT in Boston. He has been awarded the Newton International Fellowship of the Royal Society in applied mathematics, he was hosted by the Centre for Advance Spatial Analysis (CASA) at University College London UCL. Before joining Northeastern University London, Riccardo was a lecturer in Data Science at the Computer Science Department of the University of Exeter.

Riccardo has been a consultant for the World Bank, he led projects in collaboration with companies and ONG (Bill & Melinda Gates Foundation, and Data2x) to develop fine-tuned data-driven solutions for policymaking in developing countries.

Riccardo is leading a multidisciplinary research group at Complex Connection Lab at Northeastern University. Riccardo's team develops innovative mathematical frameworks to analyze and model the complex social connections that govern human behavior and interactions within cities and online. Riccardo's research utilizes network theory, complex systems computational social science, and machine learning methods to investigate the digital footprints left by individuals in their daily routines.

Riccardo's research questions are built in close relationships with data providers and institutions. Using mobile phone data we decoupled the changes in human mobility across the UK during the Covid-19 health crisis; provided data-driven approaches for spatial planning, poverty reduction, and disaster resilience in developing countries such as Nigeria and Mexico. Leveraging Credit Card Data we identify socio-economic lifestyles within cities in developing countries and define gender economic and spatial routines in cities.

Currently, Riccardo's Lab is developing new Complex Systems and Machine Learning models to capture digital human traces to understand urban segregation. Additionally, they are studying the structure of social network conversations, to observe how major events affect society, conversation patterns, and users' interactions with respect to unperturbed discussion patterns.

Featured Publications

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Changes in the time-space dimension of human mobility during the COVID-19 pandemic
Santana C., Botta F., Barbosa H., Privitera F.,Menezes R. & Di Clemente, R.
TBP Nature Human Behaviour
(2023) [pdf]

Self-induced consensus of Reddit users to characterise the GameStop short squeeze
Mancini, A., Desiderio, A., Di Clemente, R. & Cimini, G.,
Scientific Reports, 12, 13780 (2022) [pdf]

Sequences of purchases in credit card data reveal life styles in urban populations
Di Clemente, R., Luengo-Oroz, M., Travizano, M., Xu, S., Vaitla, B. & González, M.C.,
Nature Communications, 9, 3330 (2018) [pdf]

Inequality is rising where social network segregation interacts with urban topology
Tóth, G., Wachs, J., Di Clemente, R., Jakobi, A, Kertész, J, & Lengyel, B.
Nature Communications, 12, 1143 (2021) [pdf]

Understanding European Integration with Bipartite Networks of Comparative Advantage
Di Clemente, R., Lengyel, B., Andersson, L-F & Eriksson, R
PNAS Nexus, 1, 5, pgac262 (2022) [pdf]

Naomi Arnold
Postdoctoral Researcher

Naomi is a Postdoctoral Research Assistant in the Network Science Institute at Northeastern University London. Her research involves making sense of massive temporal networks through algorithm design and statistical inference, which she has applied to a number of domains including financial transaction networks, online social networks and language networks. Previously, she did her PhD in Computer Science at Queen Mary University of London.

Biography

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Naomi is a Postdoctoral Research Assistant at the Network Science Institute of Northeastern University in London. Her research focuses on making sense of large connected systems by representing them as temporal graphs and developing algorithms and tools for their analysis. Most recently, this has been applied to using temporal motifs to understand cryptocurrency transaction networks and communication patterns in online social networks. Previously, she was a researcher for the Raphtory project funded by the Alan Turing Institute’s Tools, Practices and Systems stream. She obtained her PhD in Computer Science at Queen Mary University of London within the Networks group, under the supervision of Richard Clegg and Raul Mondragon.

Her research has been presented at a number of national and international venues including NetSci, the Conference on Complex Systems and ACM CSCW, and in 2019 she was awarded best PhD talk at the Cosener’s Multi-Service Networking workshop. In 2023 she co-organised the first workshop on Networks and Time based in London.

Alongside her main research she is a part of the open source community: she is a developer for the FETA (Framework for Evolving Topology Analysis) and Raphtory open source software for temporal network modelling and analysis, respectively.

Featured Publications

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In progress: Mining local and global temporal motifs from cryptocurrency transaction networks
See first section of this video for presentation of work in progress.

Moving with the Times: Investigating the Alt-Right Network Gab with Temporal Interaction Graphs
Arnold, Naomi A., et al.
Proceedings of the ACM on Human-Computer Interaction 5.CSCW2 (2021): 1-17.

Likelihood-based approach to discriminate mixtures of network models that vary in time
Arnold, Naomi A., Raul J. Mondragón, and Richard G. Clegg
Scientific reports
11.1 (2021): 5205.

Iacopo Iacopini
Assistant Professor

Iacopo Iacopini is an Assistant Professor in the Network Science Institute at Northeastern University London. His research interests are in the area of complex networks and computational social sciences, with a focus on behavioural contagion, discovery processes, group and team dynamics.

Biography

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Iacopo Iacopini is an Assistant Professor in the Network Science Institute at Northeastern University London. His research interests are in the area of complex networks and computational social science, with a focus on adoption processes, seeding/targeting strategies, and group and team dynamics. In parallel to his core research on human behaviour, he is also working on urban networks and animal social networks.

Iacopo combines theoretical developments with empirical analyses to study the role of social interactions in the dynamics of behavioural contagion, group formation and coordination, norm emergence, discovery, and innovation. His major contributions include studies on the emergence of novelties from random processes of network exploration, and how social interactions can affect their pace of discovery. Going beyond pairwise networks, Iacopo investigated the impact of group interactions on social dynamics, showing how higher-order approaches can lead to critical mass effects, sustain social contagion, and help minorities of committed individuals to overturn social conventions. He co-authored a major review and a perspective article on higher-order networks. For his early-career contributions to complexity science, he received the Emerging Researcher Award from the Complex System Society.

A physicist by training, Iacopo holds a PhD in Mathematics from Queen Mary University of London, where he worked across the Complex Systems and Networks and the Dynamical Systems and Statistical Physics groups. Prior to joining Northeastern London, he was awarded the JSMF Postdoctoral Fellowship by the James S. McDonnell Foundation, hosted at the Department of Network and Data Science at Central European University. He has held research positions at the Centre de Physique Théorique of Aix-Marseille University, at the Urban Dynamics Lab of the Centre for Advanced Spatial Analysis at University College London, and at The Alan Turing Institute --the UK national institute for Data Science and Artificial Intelligence. Previously, he worked at the ISI Foundation and at the United Nations - Universal Postal Union as a Data Scientist.

Featured Publications

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Network dynamics of innovation processes
Iacopini, I., Milojević, S., & Latora, V. (2018).
Physical Review Letters, 120(4), 048301. 

Interacting discovery processes on complex networks
Iacopini, I., Di Bona, G., Ubaldi, E., Loreto, V., & Latora, V. (2020).
Physical Review Letters, 125(24), 248301.

Simplicial models of social contagion
Iacopini, I., Petri, G., Barrat, A., & Latora, V. (2019).  
Nature Communications, 10(1), 2485. 

Non-selective distribution of infectious disease prevention may outperform risk-based targeting
Steinegger, B., Iacopini, I., Teixeira, A. S., Bracci, A., Casanova-Ferrer, P., Antonioni, A., & Valdano, E. (2022).  
Nature Communications, 13(1), 3028. 

Group interactions modulate critical mass dynamics in social convention
Iacopini, I., Petri, G., Baronchelli, A., & Barrat, A. (2022).  
Communications Physics, 5(1), 64.

Zsófia Zádor
Postdoctoral Researcher

Zsófi Zádor is a Postdoctoral Research Assistant in the Network Science Institute at Northeastern University London. Zsófi has submitted her PhD at the University of Greenwich on the topic of trade networks. Her research interests include economic networks and inequality in human dynamics. Her recent interest is understanding gendered differences in mobility.

Biography

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Zsófi Zádor (she/her) is a Postdoctoral Researcher in the Network Science Institute at Northeastern University London. With a blend of expertise in social sciences and network science, Zsófi is interested in understanding inequality in human dynamics, particularly in cities and regions. Her recent focus is on examining gendered disparities in mobility.

Zsófi has a background in economic geography and economic networks. She completed her PhD at the University of Greenwich. Her doctoral research centred around trade networks, employing a combination of economic theory and a network perspective to understand the embeddedness and brokering role of regions in Global Value Chains. Previously, she earned an MA from the Central European University and was a member of the ANET Lab, Budapest. It was during this formative period that she became passionate about combining theories of economics and geography with approaches from network and computational science.

Featured Publications

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A Weighted and Normalized Gould–Fernandez brokerage measure
Zádor Z, Zhu Z, Smith M, Gorgoni S (2022)
PLOS ONE 17(9): e0274475.

NetSI-Roux
NetSI-Roux will focus on expanding research in network epidemiology, computational epidemiology, population health, urban science, climate science, network medicine and surveillance science with network science strategies, machine learning, data science, and data visualization. The NetSI-Roux partnership presents a unique opportunity to develop innovative network modeling approaches to translational, entrepreneurial, and community focused challenges.

NetSI will participate in Roux's startup and incubator community where our team will contribute network science-driven perspectives and tools to support challenges in health, climate, urban planning and other sectors. Research applications will be focused on developing modeling frameworks, artificial intelligence approaches, data and analytics technology architectures to advance research and support decision makers in health sciences, mobility and transportation systems, socio-economic networks, ecological and climate systems.

JK Rofling

People

Matteo Chinazzi
Research Associate Professor

Matteo Chinazzi is a Research Associate Professor at Northeastern University (Department of Physics), Roux Institute member, and Core Faculty at the Network Science Institute. He conducts research at the intersection between network science, data science, epidemiology, economics, and artificial intelligence. His research interests include: a) the development of computational and analytical models to study and forecast the spatial spread of infectious diseases; b) the development of agent-based models to create realistic representations of population dynamics; c) the study of human mobility and contact patterns using high-resolution large-scale de-identified location data; d) the development of computational frameworks that combine mechanistic epidemic models with machine learning/deep learning models; and e) the study of the evolution and structure of science and innovation.

Full Biography

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Featured Publications

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