path_boost: A Python Package for Interpretable Graph-Level Prediction using Path-Based Gradient Boosting

Improving Molecular Property Prediction in Small Language Models Using Graph-based Tools

A modular Context-Augmented Prompting framework that enables agentic tool use at inference time: a trained GNN expert model provides a predictive hint with confidence, and a GNN extracts an instance-specific explanatory subgraph via a necessity-based edge-drop intervention.

NodeJEPA: Structure-Conditioned Latent Prediction for Node-Level Graph Self-Supervised Learning

Self-supervised learning on graphs is largely shaped by contrastive methods that depend on carefully designed augmentations, and by generative methods that reconstruct node attributes in the input space. Both paradigms can entangle representations with low-level input statistics rather than with relational structure. Joint-embedding predictive architectures (JEPA) instead learn by predicting latent targets rather than reconstructing inputs. Recent work has explored this idea for graph-level representation learning, but how to design JEPA-style objectives for node-level tasks, and which structural signals the predictor should condition on, remains less clear. We present NodeJEPA, a joint-embedding predictive architecture for node-level graph self-supervised learning. NodeJEPA masks structure-aware k-hop ego-subgraphs and trains a context encoder to predict the latent representations of the masked nodes. These targets come from an EMA-updated target encoder with stop-gradient. A structure-conditioned predictor integrates spectral and centrality descriptors through cross-attention. Variance, covariance, and Laplacian spectral regularizers help stabilize the embedding geometry, and an optional curriculum gradually increases masking difficulty during training. Because prediction occurs in latent space, NodeJEPA does not rely on input reconstruction or hand-crafted graph augmentations. We evaluate NodeJEPA on standard node classification benchmarks under linear probing and fine-tuning protocols, and conduct ablations on masking, prediction, and regularization design choices. Our study offers a practical recipe for node-level JEPA-style latent prediction on graphs, and clarifies when structural conditioning helps representation learning. Code, configurations, and evaluation scripts are publicly available at https://github.com/OliverZ-dot/Node-Jepa.

ProgNet: Program-Grounded Evidence Composition for Interpretable Graph Classification

ProgNet, a graph learning framework for interpretable graph classification that treats explanatory structures as first-class, reusable components of the prediction mechanism, introduces a paradigm where reasoning is grounded in a shared vocabulary of reusable structural programs.

On the Edit Path to GNN Decisions

Learning from Prior Experiments: Meta-learning Models of Workflow Performance

Evaluating the performance of machine learning workflows is a major computational bottleneck in automated machine learning (AutoML), particularly for complex pipelines involving preprocessing, model selection, and hyperparameter optimization. This work aims to develop an efficient performance prediction framework that estimates the expected accuracy of candidate machine learning workflows on unseen datasets without requiring explicit model training. We formulate performance prediction as a meta-learning regression problem that leverages historical experimental results from the OpenML platform. Machine learning workflows are represented as structured pipelines and encoded using text-based vectorization techniques, including TF-IDF, count-based, and hashing vectorizers, as well as LLM BERT embeddings. These workflow descriptors are combined with dataset-level meta-features capturing basic statistical and structural properties. Several regression models are evaluated as meta-learners, including linear models, decision trees, random forests, Gaussian processes, and gradient-boosted decision trees. The approach is systematically evaluated on the OpenML-CC18 benchmark suite using cross-validation over more than 100,000 workflow-dataset evaluations. The proposed framework achieves strong predictive performance across a wide range of workflows and datasets. In particular, gradient-boosted decision tree regressors combined with standard TF-IDF representations of workflows consistently yield the best results, reaching an average coefficient of determination \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R^2$$\end{document} of approximately 0.8 on unseen test data. While transformer-based MiniLM embeddings were evaluated, they did not consistently outperform sparse TF-IDF representations and incurred higher computational cost. Feature ablation studies indicate that restricting vocabulary size degrades performance, while extending representations with bigrams provides only marginal gains at substantially higher computational cost. The results demonstrate robust generalization across heterogeneous workflows and dataset characteristics. This study shows that accurate performance prediction for classical machine learning workflows can be achieved through meta-learning using readily available OpenML meta-data. The proposed approach enables rapid and computationally efficient estimation of workflow performance, making it well suited for accelerating AutoML search and model selection. The results indicate that careful selection of regression models is more critical than increased representational complexity, with simple and scalable workflow encodings yielding the most robust performance. Given its scalability and flexibility, the framework provides a strong foundation for future extensions incorporating richer dataset descriptors, larger meta-datasets, and more expressive embedding and regression models.

VitaGraph: building a knowledge graph for biologically relevant learning tasks

VitaGraph is presented, a comprehensive multi-purpose biological knowledge graph built by integrating and refining multiple public datasets and enabling benchmarking of graph-based models and offering the opportunity to tackle tasks such as drug repurposing, PPI prediction, and side-effect prediction, among others.