Hugging Face Trending Papers
Message-passing graph persistence
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arXiv AI
1d ago
Message-passing graph persistence
The paper introduces Message‑Passing Graph Persistence (MGP), a new family of topological descriptors that integrates message‑passing into graph filtrations. By updating node embeddings at each filtration step and using them to determine persistence events and component representatives, MGP remains invariant under graph isomorphism and is proven to be strictly more expressive than existing PH‑based descriptors. Experiments on expressivity benchmarks and various graph classification and regression datasets demonstrate that MGP outperforms other PH‑based methods on most tasks.
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...
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...
arXiv Machine Learning
Jun 18
Unreduced Persistence Diagrams for Topological Machine Learning
arXiv:2507. 07156v2 Announce Type: replace-cross Abstract: Supervised machine learning pipelines trained on features derived from persistent homology have been experimentally observed to ignore much of the information contained in a persistence diagram.
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.
arXiv Machine Learning
Sep 10
Topology-induced Operators Reveal Complementary Graph Representations without Training
arXiv:2609.08152v1 Announce Type: new Abstract: Graph representation learning has largely focused on designing increasingly sophisticated models to transform graph topology into vector representation...