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 (https://www.sigapp.org/sac/sac2022/), 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. The paper can be found here: Using Local Activity Encoding for Dynamic Graph Pooling in Stuctural-Dynamic Graphs, Continuous-Time Generative Graph Neural Network for Attributed Dynamic Graphs

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 winning talks here:

Silvia Beddar-Wiesing '"Using Local Activity Encoding for Dynamic Graph Pooling in Stuctural-Dynamic Graphs"' (unterlegt von link https://www.youtube.com/watch?v=WuC5GFQwSlM)

Alice Moallemy-Oureh "Continuous-Time Generative Graph Neural Network for Attributed Dynamic Graphs" (unterlegt von link https://www.youtube.com/watch?v=nkDYaNOIQ5M)