Post #824

GraphML News (Feb 10th) - TensorFlow GNN 1.0, New ICML submissions 🔧 The official release of TensforFlow-GNN 1.0 by Google (after several road show presentations from the team at ICML and NeurIPS) - production-level library for training GNNs on large graphs with the first-class citizen support for heterogeneous graphs. Check the blog post and github repo for more practical examples and documentation ⚛️ The Denoising force fields repository from Microsoft Research for diffusion models trained on coarse-grained protein dynamics data - you can use it for standard density modeling or extract force fields from coarse-grained structures to use in Langevin dynamics simulations. The repo contains several pre-trained models you can play around with. The ICML deadline has passed and we saw a flurry of cool new preprints submitted to arxiv this week. Some notable mentions: 🐍Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces by Chloe Wang et al: state space models like Mamba are all the rage those days in NLP and CV (although so far attention still rules), this is a nice adaptation of SSMs to graphs, tested on the LRGB! 🗣️Let Your Graph Do the Talking: Encoding Structured Data for LLMs by Bryan Perozzi feat. Anton Tsitsulin present GraphToken (extension of Talk Like a Graph, ICLR 2024): using trainable set- or graph encoders to get soft prompt tokens improves the performance of frozen LLMs in answering natural language questions about basic graph properties. The last resort of hardcore graph mining teams jumps into LLMs 🗿 ⏩Link Prediction with Relational Hypergraphs by Xingyue Huang feat. Pablo Barcelo, Michael Bronstein, and Ismail Ceylan: extends conditional message passing models like NBFNet to relational hypergraphs (dubbed HC-MPNN) with nice theoretical guarantees and impressive inductive performance boosts. 📈Neural Scaling Laws on Graphs by Jingzhe Liu feat. Neil Shah and Jilian Tang: one of the first systematic studies of scaling laws for graph models (GNNs and Graph Transformers) and data (mostly OGB datasets) where the number of edges is selected as the universal size metric. Basically, scaling does happen but with certain nuances as to model depth and architecture (transformers seem to scale more monotonically). The church of scaling laws opens its doors to the graph learning crowd ⛪ 📚On the Completeness of Invariant Geometric Deep Learning Models by Zian Li feat. Muhan Zhang: theoretical study of DimeNet, GemNet, and SphereNet with the proofs of their E(3)-completeness through the nested GNN extension (Nested GNNs from NeurIPS’21) 📚On dimensionality of feature vectors in MPNNs by Cesar Bravo et al - turns out the WL-MPNN equivalence holds even for 1-dimensional node features when using non-polynomial activations like sigmoid. Next time, we’ll look into some new position papers.