GD2026 PhD School
Lecturers
Prof. Dr. Reyan Ahmed
University of Arizona, United States
Title: Graph sparsification and its application to network visualization
Abstract:
we explore recent advancements in large network visualization through the lens of graph spanners and related techniques. The first topic introduces a generalized multi-level sparsification approach that enhances connectivity among vertices with varying importance, extending traditional methods such as spanning trees and Steiner trees. The second topic focuses on the construction of spanners in weighted graphs, addressing the challenges of preserving distances with additive errors while demonstrating how classic spanner constructions can be effectively adapted for improved efficiency. Next, we present a novel algorithm for creating scalable, readable tree layouts, emphasizing the minimization of edge crossings and label overlaps while optimizing edge lengths and compactness. Collectively, these topics provide valuable insights into leveraging graph spanners for the effective visualization of complex relational datasets and enhancing our understanding of large networks. Finally, we present an approach that optimizes multiple readability criteria simultaneously in network visualizations. Unlike traditional visualization methods that focus on a single criterion, this approach flexibly supports both smooth and non-smooth optimization objectives, including stress, edge lengths, neighborhood preservation, and angular resolution. Experimental results demonstrate that the approach improves graph readability and produces high-quality layouts across diverse visualization tasks.
Biography:
Reyan Ahmed is an assistant professor at the computer science department of the University of Arizona. He received his Ph.D. from the same department in 2021. Before that he has received his M.Sc. and B.Sc. from the department of computer science and engineering of Bangladesh University of Engineering and Technology. His research interests include graph algorithms, network visualization, and data science.
Prof. Dr. Alessandra Tappini
University of Perugia, Italy
Title: Hybrid Graph Visualizations: From Theory to Practice and Back
Abstract:
Hybrid graph visualizations combine the classical node-link paradigm with alternative drawing styles within a single layout. Node-link diagrams are used to show the global structure of a network, while dense portions are represented using other paradigms, such as adjacency matrices or chord diagrams, to mitigate the visual clutter caused by edge crossings. This lecture will present a research perspective on hybrid graph visualization as a graph drawing topic at the intersection of theory and practice. It will discuss how practical visualization challenges motivate new theoretical questions, and how algorithmic and combinatorial foundations can guide the design of effective visualization techniques. Through selected examples from the literature, we will examine key models, algorithmic problems, and experimental user evaluations for hybrid visualizations, highlighting both established results and open challenges.
Biography:
Alessandra Tappini is an Assistant Professor in the Department of Engineering at the University of Perugia. Her research focuses on graph drawing from both theoretical and applied perspectives, with additional interests in algorithmic graph theory and information visualization. She earned her PhD in Industrial and Information Engineering from the University of Perugia in 2020 under the supervision of Giuseppe Liotta. From 2020 to 2025, she served as a postdoctoral researcher at the same institution.
Prof. Dr. Carola Wenk
Tulane University, United States
Title: Geometric Graph Similarity: Distances, Matchings, and Algorithms
Abstract:
Geometric graphs arise naturally in applications such as road and river networks, transportation systems, biological structures, trajectories, plant morphology, and commodity networks. Comparing such graphs requires distance measures that capture both geometry and topology while remaining robust to noise, different levels of detail, and non-isomorphic graph structure. This lecture will survey distance measures for embedded and immersed graphs, including planar embedded graphs and graphs with well-behaved crossings. We will discuss algorithmic approaches and hardness results, with a particular focus on the mappings or matchings induced between the graphs, rather than only on the resulting distance value. We will also highlight approaches from topological data analysis that define signatures and distances for comparing geometric graphs. The lecture will conclude with open problems and challenges in developing distances that are mathematically well-founded, computationally tractable, and useful in applications.
Biography:
Carola Wenk is a Professor of Computer Science at Tulane University. Her research is in computational geometry, with a focus on algorithms for shape matching, curves, trajectories, and geometric graphs. She is particularly known for her work on the Fréchet distance and related similarity measures, including applications to map matching, trajectory analysis, and comparison of embedded geometric structures. Her work combines algorithmic foundations with applications in geospatial data analysis, movement modeling, and biomedical imaging.