Fresh ICLR’24 Submissions

OpenReview has finally opened all submissions on OpenReview! Here is a fresh batch of papers I found interesting:

Diffusion-based generation:

Plug-And-Play Controllable Graph Generation With Diffusion Models
Sparse Training of Discrete Diffusion Models for Graph Generation
GraphMaker: Can Diffusion Models Generate Large Attributed Graphs?
Graph Generation with Destination-Predicting Diffusion Mixture
DIFUSCO-LNS: Diffusion-Guided Large Neighbourhood Search for Integer Linear Programming
Graph Generation with K2 Trees

Proteins:

EquiPocket: an E(3)-Equivariant Geometric Graph Neural Network for Ligand Binding Site Prediction
DiffDock-Pocket: Diffusion for Pocket-Level Docking with Sidechain Flexibility
DiffSim: Aligning Diffusion Model and Molecular Dynamics Simulation for Accurate Blind Docking
Rigid Protein-Protein Docking via Equivariant Elliptic-Paraboloid Interface Prediction

Crystals and Material Generation:

Space Group Constrained Crystal Generation
Scalable Diffusion for Materials Generation
Hierarchical GFlownet for Crystal Structure Generation
MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design

Equivariant nets:

Generalizing Denoising to Non-Equilibrium Structures Improves Equivariant Force Fields
Orbit-Equivariant Graph Neural Networks
E(3) Equivariant Scalar Interaction Network
Rethinking the Benefits of Steerable Features in 3D Equivariant Graph Neural Networks
Clifford Group Equivariant Simplicial Message Passing Networks

Theory, Weisfeiler & Leman go:

G2N2: Weisfeiler and Lehman go grammatical
Attacking Graph Neural Networks with Bit Flips: Weisfeiler and Lehman Go Indifferent
Beyond Weisfeiler-Lehman: A Quantitative Framework for GNN Expressiveness
How Graph Neural Networks Learn: Lessons from Training Dynamics in Function Space

New GNN architectures:

How Powerful are Graph Neural Networks with Random Weights?
Non-backtracking Graph Neural Networks
Neural Priority Queues for Graph Neural Networks (GNNs)

Graph Transformers: too many, too similar 😅
LLMs + Graphs: tons, I'd better stay away 🫠