arXiv Machine Learning August 11, 2026 By Isuru Herath, Arin Gopakumar, Sharan Sahu

HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models

Read the original on 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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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Graph Alignment for Benchmarking Graph Neural Networks and Learning Positional Encodings

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Learning Long Range Spatio-Temporal Representations over Continuous Time Dynamic Graphs with State Space Models

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arXiv AI

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GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning

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arXiv AI

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Plain Transformers are Surprisingly Powerful Link Predictors

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arXiv Machine Learning

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CoRe-GNN: Multilevel Message passing on Coarsened graphs

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By Antonin Joly, Nicolas Keriven, Aline Roumy

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arXiv Machine Learning

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Distance-Preserving Embeddings in Inhomogeneous Random Graphs

arXiv:2607. 10074v1 Announce Type: new Abstract: Graph machine learning provides powerful tools for understanding complex networks and learning meaningful node representations.

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Graph Alignment for Benchmarking Graph Neural Networks and Learning Positional Encodings

Learning Long Range Spatio-Temporal Representations over Continuous Time Dynamic Graphs with State Space Models

GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning

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