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                                                    "Predicting graphs"




 Wednesday, 25 May 2022

    Video-Recording for any system with MP4-support

   - Video.mp4  (ca. 486 Mb)

 15:15 – 16:30



                                           SMC Colloquium lecture


                                                   Aasa Feragen

                                                     (Technical University of Denmark)



Graphs are everywhere! In anatomy and biology, they appear as transportation

systems for air, water, nutrients, or signals, and are found both on the large scale

of arteries and airways, and on the small scale of neurons in the brain.

The structure, geometry and state of the networks affect their function, and

therefore also the health of nearby tissue. Conversely, the state of surrounding

tissue also affects the networks, making them both first and second order

reporters of health, disease and dysfunction. As a consequence, networks are

studied extensively in both biology and medicine — and as a proxy for these,

in imaging.


In this talk we first discuss a well known space of graphs, where networks are

modelled as equivalence classes of adjacency matrices modulo the action of the

node permutation group. We derive geometric properties of this space and

discuss the implications of those geometric properties for statistics such as

dimensionality reduction and graph-valued regression. Next, we discuss the

potential for carrying these geometric insights with us into the realm of deep

learning on graphs.


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