NEtwoRks, Data, and Society (NERDS)

Roberta is a mentor and speaker at the Science of Science Summer school
2022-08-09 15:00 - 2022-08-10 21:00 (Europe/Copenhagen)
Roberta is one of the invited speakers and mentors at the (online) Science of Science Summer school. On August 9th, 2022 she will give a talk and participate in a panel, and she will mentor students on August 9th and 10th

Invited talk: Roberta Sinatra at SICSS-Aachen-Graz (online)
2022-07-25 16:00 - 17:30 (Europe/Copenhagen)
Roberta will give an invited talk at the (virtual) Summer Institutes of Computational Social Science – Graz Aachen edition:
Quantifying the dynamics of impact in science and art. The unprecedented availability of large-scale datasets about human dynamics has advanced our understanding of social collective phenomena quantitatively.
In this talk, we study the collective phenomenon of success and present a series of findings from the analysis of large-scale datasets of careers in science and art. By using concepts of statistical physics, complex systems, network science, and data science, we will tackle these three questions:
How does impact evolve in a career? How can we untangle luck from ability? What is the role of social networks in achieving high impact?

Roberta is an invited speaker and faculty at the Complexity-GAINs International Summer School
2022-07-06 08:00 - 2022-07-08 17:00 (Europe/Copenhagen)

Keynote talk Michael Szell at Alan Turing Institute
2022-06-15 11:20 - 12:05 (Europe/Copenhagen)
Establishing a quantitative science of bicycle networks
Making transportation in cities sustainable is one major key to solving the climate, urban livability, and urban transport crises. To this end, urban data science offers new tools to quantify societal problems in cities and to guide policy makers with human-centric solutions. In this talk I outline our recent and ongoing efforts towards that end, focusing on vulnerable road users and active travel, in particular towards establishing a data-driven science of bicycle networks. I first discuss inequalities between transport modes through mobility space and collision threat analysis, then present new computational methods based on network science to grow and plan bicycle networks optimally and equitably for various urban development stages. All research comes with open code and data, and with concrete policy recommendations towards better urban livability, public health, and decarbonization.

Roberta gives a Keynote at Nordic Network for Science of Science
2022-06-13 13:00 - 14:00 (Europe/Helsinki) | center for philosophy at University of Helsinki, Helsinki, Finland
The Nordic research councils in the Humanities and Social Sciences have provided generous funding to establish the Nordic Network for Science of Science and to run a series of workshops about the latest research on the science of science, focusing on the research scene within the nordic countries. Roberta is giving a keynote at the first workshop, hosted in Helsinki, about “Quantifying the biases of scientific impact“.

NERDS SEMINAR: Rossano Schifanella, University of Turin
2022-06-08 12:30 - 13:30 (Europe/Copenhagen) | ITU, 3F07, Kaj Munks Vej 11, Copenhagen, 2300, Denmark
Revealing the determinants of gender inequality in urban cycling with large-scale data
by Rossano Schifanella, Associate Professor in Computer Science at the University of Turin.
Abstract
Cycling is an outdoor activity with massive health benefits and an effective solution for sustainable urban transport. Despite these benefits and the recent rising popularity of cycling, most countries still have a negligible uptake. This uptake is especially low for women: there is a largely unexplained, persistent gender gap in cycling. To understand the determinants of this gender gap in cycling at scale, here we use massive, automatically collected data from the tracking application Strava on outdoor cycling for 61 cities across the United States, the United Kingdom, Italy, and the Benelux area. Leveraging the associated gender and usage information, we first quantify the emerging gender gap in recreational cycling at city level. A comparison of cycling rates of women across cities within similar geographical areas unveils a broad range of gender gaps. On a macroscopic level, we link this heterogeneity to a variety of urban indicators and provide evidence for traditional hypotheses on the determinants of the gender-cycling gap. We find a positive association between the female cycling rate and urban road safety. On a microscopic level, we identify female preferences for street-specific features in the city of New York. Enhancing the quality of the dedicated cycling infrastructure may be a way to make urban environments more accessible for women, thereby making urban transport more sustainable for everyone.

NERDS SEMINAR: Marina Georgati, AAU
2022-06-01 12:30 - 13:30 (Europe/Copenhagen) | ITU, 3F07, Kaj Munks Vej 11, Copenhagen, 2300, Denmark
Machine learning for cohort-based high-resolution spatial population projections
Marina Georgati, Aalborg University CPH
Abstract
Cities expand rapidly with international migration significantly contributing to urban growth and urban population change. However, cities miss out on a great opportunity of reclaiming valuable knowledge on future population distribution due to the lack of established tools and methodologies to project where it is more likely for people of specific socio-demographic groups to set up home. The present work suggests that spatially explicit projections can play a significant role as a tool for urban planning and for managing diversity creatively, especially when a combination of social, demographic, and topographic data is utilized. Machine learning techniques have demonstrated capabilities to capture relationships among this plethora of urban features to estimate future population distribution. We present a flexible, ML-based methodology for high-resolution gridded population projections by demographic characteristics, while combining various socio-demographic and topographic input layers.

Bio
PhD fellow with background in architecture and geoinformation. I am interested on working at the intersection of socio-economic studies and computer sciences. I am currently exploring Machine Learning solutions for cohort-based spatial population projections. Passionate with urban analysis, demography and programming, I focus on migration patterns and their spatio-temporal variations at local level in European cities within the scope of the H2020 FUME Project.

Roberta giving a keynote at INAS
2022-05-26 - 2022-05-27 | eui florence, Italy
Roberta gives a keynote talk entitled Quantifying the biases of scientific success at the 14th annual International Conference of Analytical Sociology

Invited Talk Anastassia Vybornova at Citizens Collective (CUSP, TU Delft)
2022-05-18 16:00 - 17:00 (Europe/Copenhagen)
Will Cycling Save Our Cities? A Story of Love and Panic

The climate crisis is putting our future in danger. Cycling is often described as the go-to “green bullet” to make transportation in cities more sustainable and to reduce our exacerbated fossil fuel consumption. But can we still win the global war on cars, or are our beloved cities (and with them, their inhabitants) doomed anyways? I will share a personal account of my journey into cycling research, which is a story of reciprocated love and well-founded panic. I invite you to join me on a bumpy ride to rethink urban mobility: Let’s pet the elephants in the room – and share our visions of being on the road.

Roberta talking about Complex Systems for Mondadori Scuola (in Italian)
2022-04-19 17:00 - 18:20 (Europe/Copenhagen)
The talk will be streamed realtime here:

Details of the event:
https://www.mondadorieducation.it/formazione-e-aggiornamento/appuntamenti/la-fisica-dei-sistemi-complessi-il-tutto-e-piu-della-somma-delle-parti/

Talk: Luca Maria Aiello at ANET Lab Seminar Series
2022-04-01 10:00 - 11:00 (Europe/Copenhagen)
Abstract
Social relationships are the key determinant of crucial societal outcomes, including diffusion of innovation, productivity, happiness, and life expectancy. To better attain such outcomes at scale, it is therefore paramount to have technologies that can effectively capture the type of social relationships from digital data. NLP researchers have tried to do so from conversational text but mostly focusing on sentiment or topic mining, techniques that fall short on either conciseness or exhaustiveness. We propose a theoretical model of 10 dimensions (colors) of social relationships that is backed by decades of research in social sciences and that captures most of the common relationship types. We trained a deep-learning model to classify text along these ten dimensions, and we reached performance up to 0.98 AUC. By applying this tool on large-scale conversational data, we show that the combination of the predicted dimensions suggests both the types of relationships people entertain and the types of real-world communities they shape.
Bio
Luca Aiello is an Associate Professor at the IT University of Copenhagen. Previously, he worked for 10 years as a Research Scientist in the industry: at Yahoo Labs in Barcelona, and at Bell Labs in Cambridge (UK). He conducts research in Computational Social Science, a field of studies that uses Social Science theories to inform solutions to Data Science problems. His work focuses on the analysis of conversations and collective behavior through the lens of machine-learning algorithms applied to large scale data. He was recently awarded the Carlsberg Young Investigator Fellowship to study how to build technologies to support collective cooperation in the face of societal dilemmas.
Sign up at: http://anet.krtk.mta.hu/seminars/

NERDS Seminar: Giulia Reggiani
2022-03-23 12:30 - 13:30 (Europe/Copenhagen)
Understanding bikeability of urban networks
Giulia Reggiani, Delft University of Technology

About the author:
Giulia Reggiani is ending her PhD in the department of Transport and Planning, Delft University of Technology. Her research in the last 4 years focused on structural properties of bicycle networks worldwide and on data driven methods for assessing bikeability. Her current interests lie in the broad domains of smart mobility, data analysis, and sustainability.
About the presentation:
Having a bicycle network that is fully separated from vehicular traffic is not realistic even for top-performing cities. Thus, cycling in most countries entails switching between streets with different length, safety, and comfort levels. As a consequence, the quality of bicycle networks should not be evaluated based on one but multiple factors and by considering the different preferences of the users between the factors.In this presentation I will define and analyse different types of bicycle networks and present findings on bicycle infrastructure development across 47 cities. Then, I present a multi-objective methodology to assess bikeability per origin-destination (OD) pair and for the entire network, useful for evaluation and planning of bicycle networks. I do so by introducing the concept of “bikeability curves” which allows to assess how bikeable a network is for for its heterogeneity of users. This methodology is showcased on two cities with different bike cultures: Amsterdam and Melbourne.

Invited Talk Michael Szell at PhyMo22
2022-03-11 09:30 - 10:00 (Europe/Copenhagen)
Towards a human-centric urban data science
Making our cities better and sustainable is key to solving the climate and urban transport crises. To this end, urban data science offers new tools to quantify societal problems in cities and to propose human-centric solutions to policy makers. In this talk I outline our recent and ongoing efforts towards that end, focusing on urban transport and vulnerable road users. I discuss inequalities between transport modes through mobility space and collision threat distributions, and automated methods based on network science to generate and merge bicycle networks and to identify their gaps, for different urban development stages. All research comes with concrete policy recommendations that significantly increase urban livability, public health, and decarbonization.

NERDS Seminar: Tom Brughmans
2022-03-02 12:30 - 13:30 (Europe/Copenhagen)
Tom Brughmans, Aarhus University: Archaeological Network Research, illustrated through a study on visual signalling networks of Medieval strongholds in Garhwal Himalaya, India.
This presentation provides a brief overview of network research in archaeology. What are the past phenomena typically studied using network methods in archaeology, and what empirical datasets tend to be represented as network data? When were these approaches adopted and how did the popularity in their application change over time?
It will illustrate some aspects if this field through a recent study of network Medieval strongholds in the Himalayas (co-authored with Nagendra Singh Rawat and Vinod Nautiyal; Rawat et al. 2021). Hundreds of strongholds are dotted throughout Garhwal Himalaya, Uttarakhand, India, occupying elevated positions on ridges and mountain tops in the Central Himalayan region. We show that new survey data allows for more complex theories about the fortification phenomenon in Garhwal Himalaya to be addressed for the first time, and we focus in particular on theories concerning visual signalling: the use of fire, smoke or light for communicating between Medieval strongholds in this Himalayan region. Do sets of strongholds form communities of small local-scale communication networks and can these be identified as small administrative units or independent chiefdoms? Can an integrated visual signalling network have existed to allow for efficient communication throughout the entirety of Garhwal Himalaya?

Relevant references
Rawat, N. S., Brughmans, T., Nautiyal, V., & Chauniyal, D. D. (2021). Networked medieval strongholds in Garhwal Himalaya, India. Antiquity, 95(381), 753–772. https://doi.org/10.15184/aqy.2021.4

Brughmans, T., & Brandes, U. (2017). Visibility network patterns and methods for studying visual relational phenomena in archaeology. Frontiers in Digital Humanities: Digital Archaeology, 4(17). doi:10.3389/fdigh.2017.00017

Bio
Tom Brughmans is an associate professor at Aarhus University’s Centre for Urban Network Evolutions (UrbNet) and Classical Archaeology. His research interests include the study of Roman economic and urban phenomena, past social networks, and visual signalling systems. He performs much of his work by applying computational methods such as network science, agent-based simulation and geographical information systems. His research projects MERCURY and SIMREC developed educational resources and case studies to make simulation studies of the Roman economy more common (Leverhulme Early Career Fellowship and Marie-Curie Individual Fellowship). His ongoing project MINERVA aims to develop a highly detailed network model of the Roman road system, and perform simulation experiments to explore the centuries-long distribution patterns revealed by Roman tableware and amphora data. Tom’s research blog houses resources for archaeological network research.

Talk Michael Szell at UrbanSys21
2021-10-28 09:00 - 10:00 (Europe/Copenhagen)
Growing cohesive urban bicycle networks
City planners worldwide are increasingly realizing that cycling can be a promising solution to their unsustainable car-centric transport systems. However, common bicycle network planning practices follow piecewise ad hoc approaches which do not take into account the transportation network and its structural complexity as a whole. Here we take a first step in exploring systematically the general geometric limitations in the development of urban bicycle networks, using tools from network science. We study the process of growing a graph triangulation between an arbitrary set of points of interest routed on a city’s existing street network. We run different variations of this growth process on 62 diverse cities, tested against a random null model. We find that growth phases tend to start with decreasing directness and connectedness followed by improvement, implying fundamental consequences to sustainable urban planning policy: To be successful, cities must invest into bicycle networks 1) with the right growth strategy, and 2) persistently, to overcome short-term deficiencies until a critical mass of bicycle infrastructure has been built up. Further, we find distinct overlaps of our synthetically grown networks in cities with well-developed existing bicycle networks, showing that our model is realistic and has the added potential to identify missing links. We grow networks from scratch because most cities on the planet have negligible bicycle infrastructure to start from, thus making our approach a generally applicable starting point for sustainable urban bicycle network planning with minimal, readily available data requirements. We release our network growth algorithms as open source, thus making them arbitrarily extendable with refinements.

Invited Talk Michael Szell at Urban Complex Systems
2021-10-27 11:00 - 12:00 (Europe/Copenhagen)
Making cities better with human-centric urban data science
Making our cities better and sustainable is key to solving the climate and urban transport crises. To this end, urban data science offers new tools to quantify societal problems in cities and to propose human-centric solutions to policy makers. In this talk I outline our recent and ongoing efforts towards that end, focusing on urban transport and vulnerable road users. I discuss inequalities between transport modes through mobility space and collision threat distributions, and automated methods based on network science to generate and merge bicycle networks and to identify their gaps, for different urban development stages. All research comes with concrete policy recommendations that significantly increase urban livability, public health, and decarbonization.

Talk Michael Szell at CRBAM21
2021-10-15 11:00 - 11:15 (Europe/Copenhagen) | IDA CONFERENCE, Kalvebod Brygge 31-33, Copenhagen, 1780, Denmark
The geometric limits of growing urban bicycle networks
City planners worldwide are increasingly realizing that cycling can be a promising solution to their unsustainable car-centric transport systems. However, common bicycle network planning practices follow piecewise ad hoc approaches which do not take into account the transportation network and its structural complexity as a whole. Here we take a first step in exploring systematically the general geometric limitations in the development of urban bicycle networks. We study the process of growing a graph triangulation between an arbitrary set of points of interest routed on a city’s existing street network. We run different variations of this growth process on 62 diverse cities, tested against a random null model. We find that growth phases tend to start with decreasing directness and connectedness followed by improvement, implying fundamental consequences to sustainable urban planning policy: To be successful, cities must invest into bicycle networks 1) with the right growth strategy, and 2) boldly, to overcome short-term deficiencies until a critical mass of bicycle infrastructure has been built up. Further, we find distinct overlaps of our synthetically grown networks in cities with well-developed existing bicycle networks, showing that our model is realistic and has the added potential to identify missing links. We grow networks from scratch because most cities on the planet have negligible bicycle infrastructure to start from, thus making our approach a generally applicable starting point for sustainable urban bicycle network planning with minimal, readily available data requirements. We release our network growth algorithms as open source, thus making them arbitrarily extendable with refinements.

Talk Anastassia Vybornova at CRBAM21
2021-10-15 10:00 - 10:15 (Europe/Copenhagen) | IDA CONFERENCE, Kalvebod Brygge 31-33, Copenhagen, 1780, Denmark
Network algorithms for the identification and classification of gaps in urban bicycle networks based on OSM data
What is the best location to build new bicycle infrastructure in a city? This planning decision can be conceptualized as an optimization problem: the goal is to find the most efficient solution – that is, the one that has the highest (positive) impact on the bicycle network at least cost. However, identifying locations that will significantly improve network quality is far from being a straightforward task. The first challenge is to put a number on network quality improvement in order to make different planning decisions comparable; the second is the often cumbersome collection and processing of data required by the chosen approach (e.g. input from large-scale user surveys).
Our data-driven, computational approach for the identification of gaps in bicycle networks simultaneously addresses both these challenges. We present an algorithm that identifies and rates (by relevance) gaps in a bicycle network, based solely on the input of topological features which are readily available as open-source data from OSM (OpenStreetMap). For this purpose, we define “gaps” as segments (of flexible maximum length) of existing streets which, if provided with a bicycle facility, improve the overall quality of the network. We assess the quality of the network based on network connectivity measures from graph theory, in particular edge betweenness centrality. We furthermore assume that cyclists prefer to use protected bicycle infrastructure and are willing to take a certain detour to maximize the percentage of route spent on bicycle facilities.
To showcase the applicability of our approach, we present the gaps identified by the algorithm in the bicycle network of Copenhagen, classified and ranked by relevance (quality impact). We compare our results with the city’s current bicycle network development plan (Cykelsti Prioriteringsplan 2017-2025) to assess the validity of our findings. Our work shows how network analysis based on open-source topological data can serve as a powerful and cost-efficient tool for decision-making support in bicycle network planning.

Talk Bojan Kostic at CRBAM21
2021-10-14 15:30 - 15:45 (Europe/Copenhagen) | IDA CONFERENCE, Kalvebod Brygge 31-33, Copenhagen, 1780, Denmark
Analysing cyclist behavior at signalized intersections using computer vision
Artificial intelligence, in particular deep learning and computer vision, is a promising new tool for automated video analysis in traffic studies. It allows for facilitated extraction of detailed data from video recordings at higher efficiency and lower costs, improved precision of obtained data, and reduced errors caused by manual video elaboration. This method has the potential to answer detailed questions on human traffic behavior which were very difficult, if not impossible, to answer previously. In this work we use such AI tools to investigate cyclist behavior at signalized intersections. We approach the problem by utilizing state-of-the-art tools for automated video analysis and apply them to detect and track road users in video recordings (cyclists, vehicles, and pedestrians) and extract their travelled trajectories. This gives us a fine-grained spatio-temporal data set of cyclist movements in the present of vehicles, pedestrians, and traffic lights regulation throughout the day. We perform clustering of the trajectories to identify various patterns of movements from different origins to destinations, and analyze their desire lines. This data set also allows for an insightful intersection safety analysis through assessing conflicts and surrogate measures of safety. We further investigate the social factor on cyclist behaviour, such as individual versus group behaviour in obeying the rules. We recorded a busy intersection in Copenhagen, Denmark for over 12 hours and recovered several thousand cyclist trajectories, uncovering insights about cyclist behaviour and factors affecting it. These results can be used in better and more efficient planning and operation of intersections with cyclist, to improve safety by better identifying critical conflict points, inadequate bicycle infrastructure, and situations at which regulations are not followed that may cause confusion and risky behaviour.

Roberta gives a seminar for Reykjavik University and the Gran Sasso Science
2021-09-22 15:00 - 16:00 (Europe/Copenhagen)
TITLE:
Quantifying biases in science

ABSTRACT:
Every day our life is made easier by efficient measures and algorithms that search and rank scientific information. Yet, these algorithms have an issue: they are trained on citations, which are ingrained with human biases. Therefore the output is inherently biased too, creating inequalities, raising concerns of discrimination, even harming economic growth. I will present research focusing on quantifying bias in publication data. The overarching goal of this research is to uncover the mathematical bias mechanisms that, given the same quality, drive different citation trajectories, and use them to create fair algorithms.

NERDS Seminar with Helge Pfeiffer
2021-09-22 12:30 - 13:30 (Europe/Copenhagen) | 2F12, Kaj Munks Vej 11, Copenhagen, 2300, Denmark
The NERDS seminar continues Wednesday (22/09/2021) with an exciting talk by Helge Pfeiffer, Assistant Professor at ITU, titled: “Everything is Connected – Leveraging Graphs in Software Engineering Research”. Please join us from 12:30-13:30 in person at EHK-2F12 (or via Zoom – regular link – for virtual attendance). The presentation abstract and presenter’s bio are below. I hope you can all make it!
Abstract
In my talk, I will present an overview over my students’ and my recent work on various aspects of software engineering, e.g., identification of critical projects in software ecosystems, license compatibility, or software reuse. In all of which, we leverage graph representations of software artefacts.
I will briefly outline current and future work and sketch challenges ranging from data availability to efficient graph representation, computation, and visualisation methods and tools.
With the latter, I would like to gather your feedback, experiences, and would like to learn from you, since you are the network experts at ITU and I am more a “standard software engineer that remembers som graph theory and likes graphs in general” :).
Also, I would like to identify areas of potential mutual interest on possible future collaborations.
Bio
Helge Pfeiffer works an assistant professor at the IT University of Copenhagen in the Research Center for Government IT. His research interests are in the areas of software engineering, software quality, software quality metrics, and technical debt. Before joining the Research Center for Government IT, he was working as software engineer for the Danish Meteorological Institute and as adjunkt at CPH Business Academy. In the former he was responsible for development and maintenance of 24/7 remote sensing applications and in the latter, he was teaching all aspects of software engineering.

ITU KSD/KCS Research Project Market
2021-09-01 16:00 - 18:00 (Europe/Copenhagen) | Aud 1, Rued Langgaards Vej 7, Copenhagen, 2300, Denmark
Project Market for Research Projects for K-CS and K-SD students at ITU

NERDS Seminar: Luca Aiello
2021-03-03 12:30 - 13:30 (Europe/Copenhagen) | Zoom NERDS
Luca Aiello, ITU (NERDS): Towards Health Surveillance with Social Media

In today’s heavily interconnected world, health crises develop rapidly and spread afar. Health crises burden not only people’s biological health but also their psychological and social spheres. By monitoring the population’s behavior and health conditions, public health bodies can plan prevention campaigns, promptly deploy targeted interventions, and adapt to people’s responses. We contributed to this stream of research by developing a Deep Learning tool for Natural Language Processing that extracts mentions of virtually any medical condition from unstructured social media text. We applied the tool to Reddit and Twitter posts, analyzed the clusters of the two resulting co-occurrence networks of conditions, and discovered that they correspond to well-defined categories of medical conditions. This resulted in the creation of a taxonomy of medical conditions automatically derived from online discussions. We validated the structure of our taxonomy against the official International Statistical Classification of Diseases and Related Health Problems (ICD-11), finding matches of our clusters with 20 official categories, out of 22. Based on the mentions of our taxonomy’s sub-categories on Reddit posts geo-referenced in the U.S., we were then able to compute disease-specific health scores which correlate with officially reported prevalences of 18 conditions.

Roberta @ UniCT for International Day of Women and Girls in Science
2021-02-11 15:00 - 17:00 (Europe/Copenhagen)
(Event in Italian)
Giovedì 11 febbraio 2021, con inizio alle ore 15:00, in occasione dell’International Day of Women and Girls in Science, il Dipartimento di Fisica e Astronomia “Ettore Majorana”, in collaborazione con il Centro Siciliano di Fisica Nucleare e Struttura della Materia #CSFNSM, ospiterà (online) l’incontro con due scienziate per esplorare percorsi di donne nella Fisica.
Ospiti dell’incontro saranno l’astrofisica Dr Simonetta Di Pippo, direttrice del United Nations Office for Outer Space Affairs #UNOOSA, e la Dr Roberta Sinatra, ricercatrice della IT University of Copenhagen, ex Allieva del DFA e Alumna della Scuola Superiore di Catania.
Interverranno la Prof.ssa Adriana Di Stefano, Delegata del Rettore dell’Università degli Studi di Catania #UniCT alle Pari Opportunità e la Prof.ssa Alessia Tricomi, Delegata del Rettore UniCT alla Terza Missione e Direttrice del #CSFNSM.
Sarà possibile assistere live all’incontro sulla pagina Facebook del #CSFNSM e sul canale YouTube del #DFA al link https://www.youtube.com/watch?v=tNA6K8NNZSI

Roberta @ Niels Bohr Institute
2021-01-29 15:00 - 16:00 (Europe/Copenhagen)
Quantifying the dynamics of impact in science and art (Remote Colloquium)

Roberta @ DTU Compute
2021-01-12 14:45 - 16:15 (Europe/Copenhagen)

Roberta @ The Asian Institute of Management
2020-11-26 18:00 - 19:30 (Asia/Manila)

Invited talk Maksim Kitsak
2020-10-30 12:15 - 13:15 (Europe/Copenhagen)
Maksim Kitsak (Delft University of Technology)
Geometric Representations of Complementarity-Driven Networks
Abstract:

Complementarity-driven networks are routinely embedded into latent metric spaces with the purpose of data analysis. We argue that these traditional geometric representations, where connected nodes are embedded close to each other in the latent space, are not applicable to complementarity-driven networks due to the contradiction between the triangle inequality in latent metric spaces and the non-transitivity of complementarity principles. We develop a cross-geometric framework for the embedding of complementarity-driven networks and demonstrate that this representation allows for accurate embedding of real complementarity-driven networks.

Bio:

Dr. Kitsak is an assistant professor in the faculty of Electrical Engineering, Mathematics and Computer Science at the Delft University of Technology. Dr. Kitsak earned Ph.D. in theoretical physics from Boston University. Dr. Kitsak has held postdoctoral positions at the Center for Applied Internet Data Analysis (CAIDA), UC San Diego; and the Center for Complex Network Research (CCNR), Northeastern University. His research focuses on the development of theoretical and computational approaches to networked systems, with diverse applications ranging from systems biology to civil infrastructure. Results of his research have been published in top cross-disciplinary journals, such as Nature, Nature Physics, Science, and Science Advances, and have received broad media coverage.

PhD Defense Obaida Hanteer
2020-06-11 13:00 - 14:00 (Europe/Copenhagen)
Thesis: “A Practical and Critical Look at the Problem of Community Discovery in Multi-layer Networks”
Examination Committee:
Associate Professor Barbara Plank, IT University of Copenhagen, Denmark (Chair)
Professor Vito Latora, University of London, England
Dr. Luca Aiello, Nokia Bell Labs, England
Supervisor:
Associate Professor Luca Rossi, IT University of Copenhagen, Denmark
Co-supervisor:
Professor Irina Shklovski, University of Copenhagen, Denmark
Pre-registration:
For participation you must register here no later than 9 June 2020 at 3 PM. Upon registration, you will receive an invitation with a link to join the online defence no later than the workday before the defence.

Roberta @ SSC Catania
2020-03-29 19:00 - 21:00 (Europe/Copenhagen)
La matematica dei contagi
