WiNS Seminar

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.

Elena Candellone, Haily Merritt , Gülşah Akçakır, Katherine Hamilton, and Ketika Garg 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

Laura W. Dozal

College of Information, University of Arizona

Title: Identifying narrative network structure from a collection of topical images.

February 9, 2026

Bio: Laura W. Dozal is a PhD Candidate at the University of Arizona’s College of Information (iSchool). She is a Computational Social Scientist with 6+ years of specialization in information behavior and visual analysis through Computer Vision, Natural Language Processing, Network Science, and knowledge discovery. Her research methods focus on exploring online social movement messaging sentiment through domain and open science expertise. Recently she's been interested in identifying scenarios to produce synthetic multimodal data using ML/AI methods for larger downstream tasks.

Abstract: This research project answers the question; how can we identify a narrative structure from a collection of topical images using computational methods. The analysis is completed in a three-step AI/ML pipeline to build a narrative structure representative of the local semantics of image groups and an overall global summarization of the narrative structure as a whole. This first step pulls feature embeddings from a collection of 16,657 Instagram images pertaining to the anti-feminicide movement in Mexico; and clusters them to identify common vector embedding groups within the data. These images have multilingual text and contain various types of visual content. Human-in-the-Loop (HITL) evaluation analysis is applied to provide a qualitative understanding of the images within each cluster group and gain insight on the classification aspects of the images for downstream tasks. The next step takes these images and runs them through foundational vision transformer models to generate two labels identified in the HITL analysis, and a description of each image. The outputs are compared and analyzed using multi-label classification metrics for machine learning models along with HITL discourse analysis of the categorization process. Step three uses the top labels created from the model with the best overall quantitative and HITL discourse evaluation metrics to build a network graph representing the various stages of semantics found within the image and its attributes: 1) labels 2) descriptions and original post comments. The graph is analyzed to identify structural patterns of the network using traditional centrality measures and a modularity algorithm (Leiden). Variational graph autoencoders using a graph attention network were run to integrate sentiment into the network’s structure and understand the narrative as a whole while taking into account its local features.

Assemgul Kozhabek

Department of Computer Science, UC Santa Barbara

Title: Complex Network Perspectives on Urban Mobility: Structure, Dynamics, and Resilience

February 23, 2026

Bio: Dr Assemgul Kozhabek is a Postdoctoral Research Associate at Heriot-Watt University and an Associate Fellow of the Higher Education Academy. She previously worked as a Data Scientist and Research Associate at Ulster University, where she applied machine learning and data analytics to industry-focused projects in smart manufacturing and robotics. Her research centres on network science, data analytics, and computational modelling of complex systems, with applications in urban infrastructure, scientific complexity, and research reproducibility. She holds a PhD in Computer Science from Bournemouth University and an MSc in Engineering Business Management from the University of Warwick. Outside academia, Assemgul enjoys exploring local destinations through slow travel and is committed to mindful, sustainable consumption in her everyday life.

Abstract: Urban road systems can be understood as complex networks whose structure strongly shapes their resilience and traffic dynamics. We analyse urban road networks using a multi-scale network science framework, examining global, meso-scale, and nodal properties across densely populated cities. At the global scale, network metrics capture efficiency and robustness, while meso-scale analysis reveals strong community structure and weak core–periphery organisation, indicating predominantly polycentric urban forms. Nodal-level centrality measures exhibit distinct yet consistent distributional patterns across cities. To assess robustness, we evaluate the impact of targeted and random node removal strategies on network connectivity and efficiency. Centrality based disruptions are shown to be significantly more effective than random failures, with shortest-path-based measures most damaging to global connectivity and local-connectivity measures primarily affecting local efficiency. Counter-intuitively, some perturbations increase local efficiency, highlighting non-trivial resilience effects. Finally, we model traffic congestion as a network-constrained spreading process using a topology-aware SIR framework. Applied to real-world traffic data, the model outperforms classical mean-field approaches, offering improved insight into congestion propagation.