Papers

• Quantitative Bounds for Sorting-Based Permutation-Invariant Embeddings (2025)
  Nadav Dym and Matthias Wellershoff and Efstratios Tsoukanis and Daniel Levy and Radu Balan
  1.44
• Learning from one graph: transductive learning guarantees via the geometry of small random worlds (2025)
  Nils Detering et al.
  1.39
• Metrics for Parametric Families of Networks (2025)
  Mario G\'omez et al.
  1.39
• Conic Formulations of Transport Metrics for Unbalanced Measure Networks and Hypernetworks (2025)
  Mary Chriselda Antony Oliver et al.
  1.33
• Spectral Contraction of Boundary-Weighted Filters on delta-Hyperbolic Graphs (2025)
  Le Vu Anh et al.
  1.22
• Expanders in Models of Social Networks (2025)
  Marc Kaufmann et al.
  1.22
• Generalized Spectral Clustering via Gromov-Wasserstein Learning (2020)
  Samir Chowdhury et al.
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• Geometric Sampling of Networks (2020)
  Vladislav Barkanass et al.
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• Diff2Dist: Learning Spectrally Distinct Edge Functions, with Applications to Cell Morphology Analysis (2021)
  Cory Braker Scott et al.
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• Weisfeiler-Lehman meets Gromov-Wasserstein (2022)
  Samantha Chen et al.
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• Strain-Minimizing Hyperbolic Network Embeddings with Landmarks (2022)
  Martin Keller-Ressel and Stephanie Nargang
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• Selection of Centrality Measures Using Self-Consistency and Bridge Axioms (2023)
  Pavel Chebotarev
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• Neural Snowflakes: Universal Latent Graph Inference via Trainable Latent Geometries (2023)
  Haitz S\'aez de Oc\'ariz Borde et al.
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• Introduction to graph theory and basic algorithms (2023)
  Mikhail Tuzhilin et al.
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• Signed graphs in data sciences via communicability geometry (2024)
  Fernando Diaz-Diaz and Ernesto Estrada
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• Sublinear Cuts are the Exception in BDF-GIRGs (2024)
  Marc Kaufmann and Raghu Raman Ravi and Ulysse Schaller
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• The Z-Gromov-Wasserstein Distance (2024)
  Martin Bauer et al.
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• Neural Spacetimes for DAG Representation Learning (2024)
  Haitz S\'aez de Oc\'ariz Borde et al.
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• Fitting trees to $\ell_1$-hyperbolic distances (2024)
  Joon-Hyeok Yim and Anna C. Gilbert
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• Geometry of the Space of Partitioned Networks: A Unified Theoretical and Computational Framework (2024)
  Stephen Y Zhang et al.
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