Hugging Face Trending Papers

Batch Before You Lift: Scalable Topological Deep Learning on Large Graphs

Batch Before You Lift: Scalable Topological Deep Learning on Large Graphs introduces Cluster‑TNN, a framework that lifts graph data locally instead of globally, enabling efficient mini‑batch construction for higher‑order domains such as hypergraphs and simplicial complexes. By partitioning the input graph and dynamically sampling node clusters at runtime, Cluster‑TNN reduces peak GPU memory usage by an average of 83.2% across 21 comparisons while preserving connectivity needed for topological structures. This approach allows the first training of multiple higher‑order Topological Neural Networks on large datasets like Reddit and OGBN Products, demonstrating a general strategy for scaling Topological Deep Learning beyond global domain construction.

arXiv AI

Oct 9

Batch Before You Lift: Scalable Topological Deep Learning on Large Graphs

Batch Before You Lift: Scalable Topological Deep Learning on Large Graphs introduces Cluster‑TNN, a framework that lifts graph data locally instead of constructing a full lifted representation. By partitioning the input graph, sampling node clusters, and reconstructing induced subgraphs at runtime, Cluster‑TNN creates topological mini‑batches that preserve necessary connectivity for higher‑order structures. Across 21 matched comparisons, it reduces peak GPU memory by an average of 83.2% while maintaining competitive predictive performance, enabling the first training of multiple higher‑order Topological Neural Networks on large datasets such as Reddit and OGBN Products.

By David Leko, Luka Beni\'c, Guillermo Bern\'ardez, Nina Miolane, Olga Fink, Lev Telyatnikov

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

Aug 18

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

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

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

Hugging Face Trending Papers

Sep 15

Repurposing Unified Topological Signatures for Graph Representation Learning

Message-passing Graph Neural Networks (GNNs) iteratively propagate and aggregate local neighborhood information followed by global readout to learn graph representations. However, their discriminative...

rag benchmarks

arXiv AI

Sep 16

Repurposing Unified Topological Signatures for Graph Representation Learning

arXiv:2609.17061v1 Announce Type: cross Abstract: Message-passing Graph Neural Networks (GNNs) iteratively propagate and aggregate local neighborhood information followed by global readout to learn g...

By Sanyam Sanjay Jain, Anshika Krishnatray, Aditya Sharma, Vinti Agarwal

rag benchmarks

arXiv Machine Learning

Oct 3

Higher-Order Positional Encodings for Graph Representation Learning

The paper introduces higher-order positional encodings that enrich graph representations by incorporating topological information from lifted incidence structures, without altering existing graph learning backbones. It theoretically shows that these encodings can mix graph Laplacian frequencies beyond what scalar spectral filters achieve, and demonstrates their effectiveness on Graph Transformers for datasets like ZINC and synthetic benchmarks. The approach bridges graph positional encodings and topological deep learning, enabling standard models to exploit higher-order interactions.

By Caleb Stam, Aagrim Hoysal, Sanjukta Krishnagopal

llms benchmarks

arXiv AI

Jul 23

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.

By Chuan-Shen Hu

diffusion

arXiv Machine Learning

Sep 30

TopoEmbedX: A General Framework for Representation Learning on Topological Domains

arXiv:2609.37884v1 Announce Type: new Abstract: Topological structures such as simplicial complexes, hypergraphs, and cell complexes extend standard graph models by modeling higher-order relationship...

By Florian Frantzen, Ibrahem AlJabea, Ines Henriques-Cadby, Theodore Papamarkou, Mustafa Hajij, Michael T. Schaub

rag

arXiv Machine Learning

Aug 4

CoRe-GNN: Multilevel Message passing on Coarsened graphs

arXiv:2608. 02128v1 Announce Type: new Abstract: Training Graph Neural Networks on large graphs is challenged by the memory cost of storing all node representations across layers.

By Antonin Joly, Nicolas Keriven, Aline Roumy

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

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