Maurizio Patrignani
Stefan Felsner

Stefan Felsner is professor for Mathematics at Technische Universit├Ąt Berlin. His research interests range from Geometric Representations of Graphs via Combinatorial Geometry, Computational Geometry, and Order Theory all the way to to Structural Graph Theory. More than 150 coauthors demonstrate that he enjoys to collaborate with students and colleagues. He likes nature and the feeling of being free.

Contact Representations of Planar Graphs - Combinatorial Structure and Algorithm X

The main player in this talk is algorithm X. In its various disguises this algorithm can be used to compute contact representations of planar graphs with squares, homothetic triangles, pentagons and other shapes. To this end the algorithm exploits combinatorial structures such as transversal structures, Schnyder woods, and five-color forests. We survey what is known about algorithm X and what remains mysterious.

Leila De Floriani
Leila De Floriani

Leila De Floriani is a professor at the University of Maryland at College Park, USA, and was previously a professor at the University of Genova, Italy. She is a Fellow of the IEEE, of the International Association for Pattern Recognition, and of the European Association for Computer Graphics, a Pioneer of the Solid Modeling Association and an inducted member of the IEEE Visualization Academy. De Floriani was the 2020 President of the IEEE Computer Society, and the editor-in-chief of the IEEE Transactions on Visualization and Computer Graphics (2015-2018). She currently serves as an associate editor of top journals in the fields of geometric modeling, shape analysis and geospatial data science, and serves, and has served, on the program committees of over 150 leading international conferences. She has authored over 300 peer-reviewed publications in geometric modeling, topology-based shape analysis, data visualization, and geospatial data processing.


Michael Hoffmann

Michael Hoffmann is a researcher and lecturer at ETH Z├╝rich, Switzerland, where he received his PhD in 2005. His research interests are in combinatorics, algorithms, and complexity---in particular, concerning graph representations---combinatorial and computational geometry, and algorithm engineering. On the practical side, he has been involved in the CGAL project since its early days and is on the CGAL editorial board. In his leisure time he enjoys playing boardgames and owns a sizeable collection of them.

Arc diagrams, flip distances, and Hamiltonian triangulations

How many edge flips are needed to transform one combinatorial triangulation into another? How many spine crossings are needed in a topological book embedding of a planar graph? Under what conditions can a given planar graph be transformed into a Hamiltonian planar graph? In this talk I will discuss connections between these three questions along with some partial answers and open problems concerning specific variations of them.

Maurizio Patrignani
Maurizio Patrignani

Maurizio Patrignani is a professor at Roma Tre University in Italy. His research interests include Graph Drawing, Computational Geometry, Information Visualization, and Networking. He wrote more than 120 research papers on these subjects, combining an application-oriented attitude with a theoretical approach. He co-chaired Graph Drawing and Network Visualization 2012 and serves in the editorial board of the journal Algorithms. He received his PhD in 2001 from Sapienza University of Rome.

Visual Analysis of Large Networks - Strategies and Challenges

The visual analysis of large networks plays a critical role in today's applications and its relevance is doomed to grow in the next future. The incredibly-vast amount of networked data produced by real-world applications poses unprecedented challenges that standard graph-visualization paradigms seem unprepared to address. Indeed, although several approaches have been proposed, an effective solution appears still elusive. In this talk we will discuss the requirements of such an analysis and we will review the most promising techniques and tools that have been proposed so far to cope with such new challenges. It will be apparent that, in addition to efficiency, visual analytics tools must also be based on a combination of abstraction and modeling. Further, the goal of producing readable representations of the inner structure of large networks has lead to formalizing several combinatorial problems that need to be addressed. Therefore, this domain has both a deep impact on applications and an intriguing theoretical appeal. We will review recent results and highlight the main open questions in this domain.