arXiv AI
Oct 27

Graph-dependent pooling: adaptive granularity in hierarchical graph neural networks

Graph-Dependent Pooling (GDPool) introduces a dynamic approach to hierarchical graph pooling, allowing each graph to determine its own pooling granularity rather than relying on fixed cluster numbers or ratios. The method employs self-attention to select graph-specific anchors and uses soft node assignment to build coarser graphs, addressing optimization conflicts identified through gradient alignment analysis. Experiments on metal‑organic framework and molecular graph datasets demonstrate that GDPool outperforms existing methods in predictive accuracy, modularity, and exhibits stable behavior across various hyperparameters.

By Bohan Yoon, Hyejin Jang
arXiv Machine Learning
5d ago

On the Generation Order in GFlowNets

The paper investigates how the order in which components are generated in GFlowNets affects learning and convergence. It shows that existing GFlowNet algorithms often fail to discover useful generation orders, and introduces a new training recipe—Learning-Order GFlowNet—that learns interpretable orders without explicit annotations. Experiments demonstrate that the new method improves convergence, and analysis of gradient signal-to-noise ratio suggests that higher SNR correlates with faster convergence and better generation orders.

By Timofei Gritsaev, Evgeny Katasonov, Ildus Sadrtdinov, Esmeralda S. Whitammer, Dmitry Vetrov