SNACK: A Sequential Notation Framework for Probabilistic Graph Generation
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
arXiv:2608. 09031v1 Announce Type: new Abstract: Graph neural networks typically propagate information through repeated message-passing layers, coupling the distance over which information travels with the number of nonlinear transformations applied.
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.
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.
arXiv:2610.32650v1 Announce Type: new Abstract: In this paper, we introduce WL-SeqVAE, a sequence-based variational autoencoder for generating graphs. The key observation behind WL-SeqVAE is that nod...
arXiv:2606. 04287v1 Announce Type: cross Abstract: Generating realistic and diverse graphs is a key problem in machine learning, with applications in molecular discovery, circuit design, cybersecurity, and beyond.
arXiv:2507.07138v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) often struggle to capture the link-specific structural patterns crucial for accurate link prediction, as their node-ce...