arXiv Machine Learning June 10, 2026 By Neha Sharma, Ritesh Sharma

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

Read the original on arXiv Machine Learning →

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.

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benchmarks

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.

By Louisa Cornelis, Johan Mathe, Louis Van Langendonck, Guillermo Bern\'ardez, Nina Miolane

benchmarks

arXiv AI

Jul 2

Multi-Label Node Classification with Label Influence Propagation

arXiv:2607. 00671v1 Announce Type: cross Abstract: Graphs are a complex and versatile data structure used across various domains, with possibly multi-label nodes playing a particularly crucial role.

By Yifei Sun, Zemin Liu, Bryan Hooi, Yang Yang, Rizal Fathony, Jia Chen, Bingsheng He

rag benchmarks

arXiv Machine Learning

1d ago

Not All Neighbors Matter: Understanding the Impact of Graph Sparsification on GNN Pipelines

arXiv:2603. 06952v2 Announce Type: replace Abstract: As graphs scale to billions of nodes and edges, graph Machine Learning workloads are constrained by the cost of multi-hop traversals over exponentially growing neighborhoods.

By Yuhang Song, Naima Abrar Shami, Romaric Duvignau, Vasiliki Kalavri

arXiv Machine Learning

Jul 7

Graph Classification via Network Usable Information: From Representation Evaluation to Structure Selection

arXiv:2607. 03587v1 Announce Type: new Abstract: We propose NetinfoGC, a framework for graph classification that extends the Network Usable Information (NUI) paradigm to graph-level learning.

By Abdullah Shaik, Anwar Said

rag benchmarks

arXiv Machine Learning

Jun 4

Identifying and Correcting Label Noise for Robust GNNs via Influence Contradiction

arXiv:2601. 17469v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) have shown remarkable capabilities in learning from graph-structured data with various applications such as social analysis and bioinformatics.

By Wei Ju, Wei Zhang, Siyu Yi, Zhengyang Mao, Yifan Wang, Jingyang Yuan, Zhiping Xiao, Ziyue Qiao, Ming Zhang

diffusion benchmarks

arXiv Machine Learning

Jun 26

Graph Neural Networks Applications Across Domains: All Insights You Need

arXiv:2606. 27202v1 Announce Type: new Abstract: Graph neural networks have moved from a niche representation-learning technique to the default model class wherever data carry relational structure.

By Abderaouf Bahi

computer-vision safety

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

Multi-Label Node Classification with Label Influence Propagation

Not All Neighbors Matter: Understanding the Impact of Graph Sparsification on GNN Pipelines

Graph Classification via Network Usable Information: From Representation Evaluation to Structure Selection

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Graph Neural Networks Applications Across Domains: All Insights You Need