Graphs in Artificial Intelligence
Neural Networks

Success in student research competition

Silvia Beddar-Wiesing and Alice Moallemy-Oureh won first and third place in the Student Research Competition of the 37th ACM/SIGAPP Symposium On Applied Computing, which took place from 25th to 29th of April 2022!

Silvia's student research abstract "Using Local Activity Encoding for Dynamic Graph Pooling in Stuctural-Dynamic Graphs" introduces a preprocessing module for handling structural-dynamic graphs via local activity encoding and subsequent pooling leading to a constant-size graph sequence. Alice' student research abstract with the title "Continuous-Time Generative Graph Neural Network for Attributed Dynamic Graphs" proposes an embedding approach for attribute-dynamic graphs in continuous-time representation using a variational Graph Autoencoder for the node embeddings whose dynamics are further described by a Gaussian regression function.
We are happy and proud of the positive feedback on their work as well as their presentations that already cover many of the topics of the GAIN project.

Have a look at their papers and the winning talks here:


We are funded by the Federal Ministry of Education and Research Germany (BMBF), under the funding code 01IS20047A, according to the 'Policy for the funding of female junior researchers in Artificial Intelligence'.

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