The Women in Network Science (WiNS) seminar is an interdisciplinary seminar with the aim to promote and showcase research by women and nonbinary researchers in network science. 

The seminar is open to everyone. Please join the mailing list to receive announcements and zoom links for upcoming seminar talks.

Alice Schwarze, Francisca Ortiz Ruiz, Echo Liu, Elena Candellone and Mari Kawakatsu convene this seminar series. Please get in touch if you are interested in presenting in the seminar or if you would like to nominate speakers.

For all scheduled talks, relevant preprints are available on our ZeroDivZero repository. Recordings of past talks can also be found in the ZeroDivZero repository and on Youtube.

Scheduled talks
Mondays - 11 am Eastern Time - on Zoom 

Bio: Michelle Girvan is a Professor in the Department of Physics at the University of Maryland (UMD). She also has appointments in the Institute for Physical Science and Technology, theInstitute for Research in Electronics and Applied Physics, and the Applied Math and Scientific Computing Program. Her research focuses on applications of network science to biological, social, and technological systems. Much of her recent work is aimed at the intersection of network science and machine learning. 

Girvan received bachelors degrees in physics and mathematics from MIT. She then went on to earn her PhD in physics from Cornell University followed by a postdoctoral fellowship at the Santa Fe Institute before joining the faculty at the University of Maryland. Girvan now serves on the Santa Fe Institute's External Faculty and Science Steering Committee. Girvan is also a Vice President of the Network Science Societyand a Fellow of the American Physical Society.

Abstract: Through "Pathways in Network Science", we aim to shed light on the exciting, diverse, and sometimes challenging career paths of women and nonbinary researchers in network science. In today's seminar, Michelle Girvan from the University of Maryland reflects on the path that led her to and through network science.

Website: https://umdphysics.umd.edu/people/faculty/current/item/173-girvan.html

Bio: My name is Laura and I am a third year PhD student at the University of Exeter’s Q-Step Centre. My current research focuses on the relationship between armed conflict in Colombia and deforestation, using innovative network analysis methods. I am passionate about studying social-ecological systems and using network analysis to understand complex relationships between nature, individuals, groups, and institutions. Through my research, I hope to contribute to a greater understanding of the ways in which human actions and societal structures interact with and impact the natural world.

Abstract: The devastating consequences of civil conflict hinder the socio-economic development of countries that endure it. Ultimately, there is no clear victory in such conflicts; however, the devastation continues to linger, affecting entire generations. Longer wars are also less likely to reach a resolution, and affected countries face significant challenges in relation to peacebuilding and an increased risk of relapse. Therefore, understanding the mechanisms that underlie long-standing, chronic conflicts may hold the key to de-escalating them and is a necessary condition for successful peacebuilding efforts. In the present study, I examine the Colombian armed conflict, which serves as a ripe example of a chronic, long-lasting civil confrontation, believed to have originated in the rural and political landscape of the 1920s. The conflict has unravelled throughout the entire territory and has primarily been fought on the periphery of relatively stable major cities, where the presence of the state is practically non-existent. I fit a relational event model (REM) to directed data on the Colombian conflict from 1960 to 2021. REM captures the dynamic process of the event history of an armed conflict, providing insight into the relative importance of mechanisms that sustain the conflict, such as reciprocity and inertia. According to the theory of interstate conflict posited by Clausewitz, the beginnings of armed conflicts are dominated by reciprocal attacks. If unresolved, violence tends to escalate to 'culminating points', where the attacks are dominated by a mechanism akin to inertia. In the case of long-standing conflicts, the duration after the culmination point is determined by fluctuations in power between the actors involved.

Website: https://lauraroldangomez.github.io/site/ 

Bio: Karina holds a BS in Applied Mathematics from Northwestern University in Chicago, USA and an MSPH in Epidemiology from Emory University in Atlanta, USA. She also previously worked as an analyst for Deloitte Consulting. In October 2023, she joined Professor Hasenauer’s group at the University of Bonn as a PhD student. 

Abstract: The uptake of COVID-19 vaccines remains low in key populations despite their high effectiveness. Epidemic models that represent vaccine decision-making psychology can provide insight into the potential impact of vaccine promotion interventions in the context of the COVID-19 pandemic and future epidemics of vaccine preventable diseases. To this end, we coupled a network-based mathematical model of SARS-CoV-2 transmission in Georgia, USA with a social-psychological vaccination decision-making model in which vaccine side effects, breakthrough infections, and other unidentified community-level factors could “nudge” individuals towards vaccine resistance while spikes in COVID-19 hospitalizations could nudge them towards vaccine willingness. Our results suggested that vaccine promotion interventions that address community-level factors influencing decision-making may have a greater ability to avert SARS-CoV-2 infections than those targeted to individuals’ own vaccination and infection history. Additionally, we found that reactive vaccine promotion interventions may have only limited prevention benefits in the short term, suggesting that attention should be paid to formulating interventions that accurately anticipate the case curve. And the preprint for her thesis paper is available here: https://pubmed.ncbi.nlm.nih.gov/37662331/

Website: https://www.mathematics-and-life-sciences.uni-bonn.de/en/group-members/people/hasenauer-group-members/karina-wallrafen 

Bio: TBA

Abstract: Oil palm (Elaeis guinensis) is a controversial crop. To assess its sustainability, we analysed the contribution of different types of plantations (smallholder, industrial and unproductive) towards meeting six Sustainable Development Goals. Using spatial econometric methods and data from 25,067 villages in Sumatra, Indonesia, we revealed that unproductive plantations are associated with more cases of malnutrition, worsened school access, more air pollution and increased criminality. We also proposed a strategy for sustainable palm oil expansion based on replanting unproductive plantations with either industrial or smallholder palm oil. Smallholder replanting was beneficial for five Goals (Zero poverty, Good health, Quality Education, Environmental preservation and Crime reduction), while the same intervention only improved two Goals in the industrial case (Zero poverty and Quality Education). Our appraisal is relevant to policymakers aiming towards the 2030 Agenda, organisations planning oil palm expansion, and retailers or consumers concerned about the sustainability of oil consumption.

Website: https://scholar.google.pt/citations?hl=pt-PT&user=VqxAml0AAAAJ

Bio: Gordana is a final year PhD student supervised by Ulrik Brandes at the Social Networks Lab of ETH Zurich. Her background is in mathematics (BSc) and computer science (BSc and MSc), which she completed at the University of Konstanz. Much to her delight as an avid sports fan, part of her PhD project lies in the intersection of networks and sports. Her general research interest comprises methods and concepts for analyzing social networks.

Abstract: Social influence describes the phenomenon whereby an individual’s behavior, opinion, or something unobservable changes due to social interactions. These dynamics can serve as a basis for an individual's position and centrality in a network. Here, I present two separate but related projects. In the first one, we propose a novel diffusion model for temporal social networks based on two antagonistic components: first, the susceptibility to be influenced (or, conversely, an inertia toward the status quo) and, second, the tendency to become independent from past influence of others. We applied the model to data from the history of the Australian Football League (AFL): the proposition that a coach is influenced by his own coaches during his playing career yields a temporal influence network, while coaching is considered an opportunity to gradually liberate from these received influences, allowing coaches to grow independent over time. In this way, we obtain a network of influence relationships considering all lineages of coaches, from which we identify the most influential coaches in AFL history. In identifying the most influential coaches, we use a derived relation which accumulates all indirect and time-respecting traces of influence, and, as such, it can be seen as part of a centrality index. While for undirected graphs the neighborhood inclusion can be considered as a structural conceptualization of centrality, for directed graphs a neighborhood-based concept first needs to be extended. This is addressed in the second project.

Website: https://sn.ethz.ch/team.html 

Bio: Currently a researcher at Marie Curie Postdoctoral Fellowship, Centre de Physique Théorique, CNRS, advisor Prof. Alain Barrat.

Abstract: TBA

Website: https://giuliacencetti.github.io 

Previous talks
(Available on Youtube)