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*                          Einladung
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*                     Informatik-Kolloquium
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Zeit:  Dienstag, 15. Juni 2021, 14:00 Uhr

Ort:   https://rwth.zoom.us/j/94452318075?pwd=WTd0eUVPNmlVaTUzUDlvWWVvNHFlQT09

Meeting ID: 944 5231 8075
Passcode: 402666

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Vortragender: Prof. Stephan Günnemann, TUM

Titel: Machine Learning for Graphs: Expressiveness, Robustness, and Scalability 

Abstract:
Graphs are a universal language to model complex systems and are 
omnipresent in the natural sciences (protein interaction networks, 
molecular graphs), engineering sciences (IoT networks, smart grids), 
social sciences (social networks), and many more. In recent years, graph
neural networks (GNNs) have started to unlock the potential of machine 
learning for the graph domain -- yet, our understanding of these 
principles is still limited. In my talk, I will address some of the core
challenges which hinder the use of GNNs in scientific and industrial 
application domains. Specifically, I will highlight (i) the limited 
expressiveness of standard GNNs and ways to enhance them by taking 
domain knowledge into account, (ii) the GNNs’ robustness properties and 
principles to ensure their reliability, making them suitable for even 
sensitive and safety-critical domains, and (iii) their lack in 
scalability along with approaches enabling efficient learning.