arXiv AI July 23, 2026 By Chuan-Shen Hu

A Sheaf-Theoretic and Topological Perspective on Complex Network Modeling and Attention Mechanisms in Graph Neural Models

arXiv:2601. 21207v4 Announce Type: replace-cross Abstract: Combinatorial and topological structures, such as graphs, simplicial complexes, and cell complexes, form the foundation of geometric and topological deep learning (GDL and TDL) architectures.

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

diffusion

arXiv Machine Learning

Jul 31

TopoFormer: Topology Meets Attention for Graph Learning

arXiv:2607. 28259v1 Announce Type: new Abstract: We introduce Topoformer, a lightweight and scalable framework for graph representation learning that encodes topological structure into attention-friendly sequences.

By Md Joshem Uddin, Astrit Tola, Cuneyt Gurcan Akcora, Baris Coskunuzer

llms rag benchmarks

arXiv Machine Learning

Jun 2

Robust Contrastive Graph Clustering with Adaptive Local-Global Integration

arXiv:2605. 28209v2 Announce Type: replace Abstract: Graph clustering is essential in graph analysis for revealing structural patterns and node communities.

By Lei Zhang, Fubo Sun, Haipeng Yang, Zhong Guan, Likang Wu

arXiv Machine Learning

1d ago

Look Before You Lift: Visual and Quantitative Diagnostics for Topological Deep Learning

arXiv:2608. 15388v1 Announce Type: new Abstract: Topological deep learning (TDL) methods rely on lifting raw data into higher-order discrete domains such as simplicial complexes, cell complexes, and hypergraphs.

By Mathilde Papillon, Guillermo Bern\'ardez, \'Alvaro Ball\'on Barreiro, Marco Montagna, R\'emi Devaux, Antoine Jardin, Nina Miolane

arXiv Machine Learning

Aug 4

Differentiable Lifting for Topological Neural Networks

arXiv:2608. 01160v1 Announce Type: new Abstract: Topological neural networks (TNNs) enable leveraging high-order structures on graphs (e.

By Jorge Luiz Franco, Gabriel Duarte, Alexander Nikitin, Moacir Ponti, Diego Mesquita, Amauri H. Souza

benchmarks

arXiv Machine Learning

Jun 5

HOPSE: Scalable Higher-Order Positional and Structural Encoder for Combinatorial Representations

arXiv:2505. 15405v3 Announce Type: replace Abstract: While Graph Neural Networks (GNNs) have proven highly effective at modeling relational data, pairwise connections cannot fully capture multi-way relationships naturally present in complex real-world systems.

By Guillermo Bern\'ardez, Marco Montagna, Louis Van Langendonck, Martin Carrasco, Amirreza Akbari, Louisa Cornelis, Mathilde Papillon, Pere Barlet-Ros, Nina Miolane, Lev Telyatnikov

benchmarks

arXiv Machine Learning

Jun 26

NervePool: A Simplicial Pooling Layer

arXiv:2305. 06315v3 Announce Type: replace-cross Abstract: For deep learning problems on graph-structured data, pooling layers are important for down sampling, reducing computational cost, and to minimize overfitting.

By Sarah McGuire Scullen, Ernst R\"oell, Elizabeth Munch, Bastian Rieck, Matthew Hirn

Related stories

TopoFormer: Topology Meets Attention for Graph Learning

Robust Contrastive Graph Clustering with Adaptive Local-Global Integration

Look Before You Lift: Visual and Quantitative Diagnostics for Topological Deep Learning

Differentiable Lifting for Topological Neural Networks

HOPSE: Scalable Higher-Order Positional and Structural Encoder for Combinatorial Representations

NervePool: A Simplicial Pooling Layer