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.

Previous talks
(Available on Youtube)

Presentation by Alice Schwarze, Francisca Ortiz Ruiz, Echo Liu, and Elena Candellone.

Abstract: We will discuss why our research community needs Women in Network Science and what our society has achieved in recent years. With the growth of its membership, Women in Network Science has become an international community with a distinct culture unique in the academic environment. We will review how this culture has developed over a wealth of online events that the society has organized between 2021 and 2023, and we will give an outlook on the opportunities and challenges that our society faces as we transition to in-person meetings again. With this look behind the scenes of the Women in Network Science leadership team, we hope to encourage audience members to consider joining our leadership team in the upcoming years.

Bio: Clara Stegehuis is an assistant professor at Twente University. She works at the intersection of probability theory, graph theory and stochastic networks, with an emphasis on asymptotic analysis, stochastic process limits, and randomized algorithms. Problems she investigates are inspired by applications in network science, physics and computer science.  

Abstract: Network motifs contain important information about network structures and their functions. But where in the network can we find them? We investigate this for several random graph models. We show that in the popular configuration model, motifs often appear on very specific degrees. On the popular hyperbolic random graph model, our optimization problem shows the trade-off between geometry and popularity: some subgraphs are most likely formed by vertices that are close by, whereas others are most likely formed by vertices of high degree. We also show that while in practice Z-scores that compare motif counts to a normal random variable are frequently used, motif counts in random graph models may not behave like normal distributions at all. 

Website: https://www.clarastegehuis.nl 

Bio: Xie He is a PhD Candidate in Applied Mathematics at Dartmouth College, advised by Peter Mucha. She holds a B.S. in Mathematics of Computation from University of California (UCLA). She is interested in connecting Network Science with ML and everything else. Whether it is brain data in biology, or social media data, or even if it is something entirely new, she wants to study the topological structure of how network could combine data points and link the world together. Specifically, she is interested in predicting the future through link prediction on temporal networks. Recently, she expanded her interest to how graph topology would be useful in NLP.

Abstract: Link prediction algorithms are indispensable tools in many scientific applications by speeding up network data collection and imputing missing connections. However, in many systems, links change over time and it remains unclear how to optimally exploit such temporal information for link predictions in such networks. Here, we show that many temporal topological features, in addition to having high computational cost, are less accurate in temporal link prediction than sequentially stacked static network features. This sequential stacking link prediction method uses 41 static network features that avoids detailed feature engineering choices and is capable of learning a highly accurate predictive distribution of future connections from historical data. We demonstrate that this algorithm works well for both partially observed and completely unobserved target layers, and achieves near-optimal AUC on two temporal stochastic block models. Finally, we empirically illustrate that stacking multiple predictive methods together further improves performance on 19 real-world temporal networks from different domains.

Website: https://sites.google.com/view/xie-he/about-me 

Bio: Marta Sales-Pardo (Barcelona, 1976) graduated in Physics at Universitat de Barcelona in 1998, and obtained a PhD in Physics from Universitat de Barcelona in 2002. She then moved to Northwestern University, where she first worked as a postdoctoral fellow and, later, as a Fulbright Scholar. In 2008, she became a Research Assistant Professor at the Northwestern University Clinical and Translational Science Institute with joint appointments in the Department of Chemical and Biological Engineering and the Northwestern Institute on Complex Systems. In 2009, she accepted her current position as an Associate Professor in the Departament d'Engineyria Química at Universitat Rovira i Virgili.

Website: https://amaral.northwestern.edu/people/sales-pardo/ 

Bio: Panayiota Poirazi (b. 1974) started out as the youngest principal investigator at IMBB-FORTH, in 2002. She joined IMBB with less than 2 years of postdoctoral experience (in Greece), where she moved upon completion of her graduate studies in the US (University of Southern California, Los Angeles, 1996-2000). She was promoted to Associate Researcher in 2008 and in 2014 she became a Research Director (equivalent to full professor), a position that she holds until today. She leads the Dendrites lab (www.dendrites.gr) at IMBB-FORTH, whose mission is to unravel the role of dendrites in complex brain functions. Her lab is one of a handful in Greece that use computational modelling approaches to study brain function and develop neuro-inspired artificial intelligence methods. In the last few years, she has expanded her research activities to include behavioral and in vivo imaging experiments in mice.

Website: http://dendrites.gr 

No seminar on October 16, 2023.

Abstract: Cultural sociologists have traditionally viewed categorical differentiation as relational “distances”, and described those structures using network terms such as “clusters” and “holes”. Yet, existing research into the notion of relational similarity often focuses on either one-mode social networks or two-mode cultural affiliation networks independently, and rarely has the cross-level interaction between cultural affiliations and social ties been explored. Here I present two sets of ongoing work addressing this gap. First, utilizing data from both one-mode social networks and two-mode cultural affiliation networks, we distinguish between two types of relational distances between cultural genres: shortened paths resulting from genres co-occurring in individuals’ choices, and shortened paths arising from genres connected through social ties between individuals. We show how the two different conceptualizations produce surprisingly different clustering patterns. Second, using longitudinal data from a student cohort, we formalize network motifs to characterize the dynamic coupling of music tastes and social networks in a multilevel framework. We show how micro network processes --- such as selection and influence --- could be linked to structural characteristics of such a socio-cultural system via an actor-based modeling approach. Through this case study, we propose that those network motifs serve as building blocks for understanding the social foundations of cultural tastes, and we discuss the implications for social network research and sociology of culture.

Bio: Xinwei Xu is currently a Postdoctoral Researcher at the Social Networks Lab at ETH Zurich, after obtaining a PhD in sociology from Cornell University. Her research focusses on understanding how networks shape processes of social and cultural differentiation in diverse settings. Her (evolving) research interest includes culture, social networks, cognition, intergroup relations, and most recently, the structural and social consequences of recommender systems. 

Website: https://sn.ethz.ch/profile.html?persid=299457 

Abstract: Regulating transcription, translation, and degradation processes is crucial for proper neuronal function, ensuring required protein levels. However, in conditions like Huntington's Disease (HD), these processes may exhibit aberrant behavior due to altered signaling pathways, resulting in either elevated or reduced mRNA and protein levels in affected animals. Understanding modifications in these processes and their collective impact on mRNA and protein dynamics is essential for unraveling the molecular mechanisms underlying HD pathology. Our study introduces a novel approach to investigate how mutant Huntington mRNA and protein disrupt protein synthesis processes and explores their combined effects under diseased conditions.

Bio: Yuhong Liu is currently a PhD student at the University of Bonn working in mathematical neuroscience, supervised by Professor Tatjana Tchumatchenko. Her research is advancing the understanding of protein dynamics in neurons that affect synaptic plasticity and how such effect plays a role in network dynamics. She collaborates closely with experimental labs at University of Siegen in Germany and Max Planck Florida Institute in US who provide state-of-the-art data on cellular synthesis dynamics under disease or normal conditions.

Website: https://echorliu.github.io 

No seminar on November 13, 2023.

Abstract: Neural connections are plastic, changing over time in a way that depends on the neural activity. The connectivity structure, in turn, influences the neural activity, creating a reciprocal interdependence that is crucial in determining the evolution of neural circuits through learning and development. However, this intricate relationship between structure and activity has yet to be fully understood. Some progress has been made by observing that non-random connectivity patterns arise in neural networks, suggesting that further insight may be obtained by uncovering the mechanisms behind the emergence of these patterns. Here I will show, through analytical calculations and numerical simulations, how different plasticity rules shape connectivity structures within neural networks. The theoretical framework is validated by simulating spiking neural networks under biologically realistic assumptions. Overall, this work illustrates how different aspects of neural activity, such as firing rates and pairwise correlations, contribute to shaping the structure and development of neural circuits.

Bio: Claudia is a PhD student in the Computation in Neural Circuits group at the Technical University of Munich. She is interested in understanding the development of neural circuits and the processes underlying their organisation. In her work, she uses mathematical analysis and computational modelling to study the effects of synaptic plasticity on the activity and connectivity structures in neural networks. Her research is supported by the Marie Sklodowska-Curie Actions Innovative Training Networks project called “Advanced simulation, analysis and interpretation of network structures in biological data” (SmartNets).

Website: https://www.smartnets-etn.eu/how-higher-order-correlations-shape-network-structure/ 

Abstract: Business and collaboration increasingly depend on interorganizational networks to access material and nonmaterial resources and to tackle complex social and environmental challenges. In this presentation, I will discuss the importance of considering gender and intersectionality in the context of these networks and how this perspective can be included when using social network analysis.

Bio: Postdoctoral Researcher at ESADE Business School, Lecturer at Eberhard Karl University of Tübingen, Student Supervisor at Vrije Universiteit Amsterdam. She is a sociologist from the University of Buenos Aires, Argentina, with interdisciplinary experience and has been a visiting researcher in Brazil, the Basque Country, and the Netherlands. During her Ph.D. studies she investigated the interorganizational networks, collaboration, gender-based inequalities, and practices in social entrepreneurship and the social and solidarity economy in Barcelona. 

Website: https://gender-ict.net/people/natalia-garrido-skurkowicz/ 

No seminar on November 27, 2023.

Bio: Brea Perry is a Professor of Sociology and an affiliated faculty of the Indiana University Network Science Institute. She began her career at the University of Kentucky before returning to Indiana University in 2014, where she received her PhD in 2008. Her research investigates the interrelated roles of social networks, biomarkers, social psychology, and social inequality in health and illness, with a particular focus on mental illness and substance use disorders. She has a strong interest in longitudinal research, dynamic social processes, and quantitative methods, especially personal social network analysis. Perry's current projects (funded by NIH and NSF) examine: 1) the social dynamics of high-risk opioid-seeking behavior; 2) the social safety nets of healthcare “super utilizers” with complex, comorbid conditions; 3) cognitive reserve and social network moderation of neurodegeneration in the aging brain; 4) stigma as barrier to recovery from opioid dependence in rural and urban communities; and 5) contributions of acculturation, social networks, and cultural health capital to the immigrant health paradox.

Brea Perry has published her research in journals such as American Journal of Sociology, American Sociological Review, Journal of Health and Social Behavior, and Social Science and Medicine. She is currently on the editorial board of Journal of Health and Social Behavior, and is the series editor of Advances in Medical Sociology. Perry recently authored a book on ego network methodology (Cambridge University Press) with Bernice Pescosolido and Steve Borgatti. She has received funding from multiple National Institutes of Health, including NIDA, NIDCR, NIA, and NCRR, as well as the National Science Foundation and several charitable foundations. When she is not being a professor, Brea enjoys spending time with her family, hiking, playing soccer, and singing karaoke.

Website: https://sociology.indiana.edu/about/faculty/perry-brea.html