💐 GraphML News 🌷

Everything you wanted to know about Clifford Layers and its applications in PDE modeling and molecular dynamics is now collected on a single website, sprinkle with the recent LoGaG presentation (video) and add a little bit of Geometric Algebra intro from bivector for the best experience.

Some freshly arxived papers you might want to grab for the weekend reading:

Can We Scale Transformers to Predict Parameters of Diverse ImageNet Models? by Knyazev et al - introduces Graph HyperNetwork v3 for predicting the weights of neural network architectures. The previous version GHN-2 got a massive recognition at NeurIPS’21 including an interview with Yannic Kilcher. Instead of training neural nets, you could use GHN to estimate model params in one forward pass and it demonstrated a non-trivial performance on ImageNet. In the new version, the authors apply a Graphormer on the model’s computation graph DAG to frame the task as node regression where node parameters correspond to weight matrices in the target neural nets. You can also use GHN for better initialization of model weights instead of random init.

SUREL+: Moving from Walks to Sets for Scalable Subgraph-based Graph Representation Learning by Yin et al - the next iteration of SUREL for link prediction where subgraphs are replaced with random walks for better scalability