arXiv Machine Learning June 4, 2026 By Pascal Plettenberg, Denis Huseljic, Andr\'e Alcalde, Bernhard Sick, Josephine M. Thomas

ALINC: Active Learning for Inductive Node Classification via Graph Sampling

Read the original on arXiv Machine Learning →

arXiv:2606. 04647v1 Announce Type: new Abstract: Active learning (AL) for node classification typically focuses on selecting the most informative nodes for annotation within one or a few large graphs (e.

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arXiv Machine Learning

Jun 2

OgBench: A Framework for Evaluating Graph Neural Networks on Omics Data

arXiv:2605. 15511v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) have become the dominant framework for inductive graph-level learning.

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arXiv Machine Learning

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Canopy: A Heterograph Foundation Model for Metabolic Engineering

arXiv:2607. 06224v1 Announce Type: new Abstract: Designing microbial strains that produce high-value chemicals at commercially viable titers remains a central challenge in metabolic engineering.

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llms rag multimodal

arXiv Machine Learning

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Benchmarking graph-based models for in-silico toxicity prediction in drug discovery

arXiv:2609.37555v1 Announce Type: new Abstract: Drug discovery is a costly and high-risk process, where toxicity-related failures remain a major cause of attrition in both preclinical and clinical st...

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benchmarks safety

arXiv Machine Learning

Jun 11

GraphInfer-Bench: Benchmarking LLM's Inference Capability on Graphs

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arXiv Machine Learning

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By Christina X. Ji

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arXiv Machine Learning

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When Design Rules Break: Benchmark Composition Determines Whether Label Informativeness Predicts GNN Aggregator Choice

arXiv:2606. 10249v1 Announce Type: new Abstract: We examine whether graph neural network (GNN) design rules generalize across benchmark families by studying aggregator selection (sum, mean, max) on 24 node-classification datasets spanning citation, heterophilic, LINKX Facebook-100, co-purchase, and co-authorship graphs.

By Neha Sharma, Ritesh Sharma

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