Papers

• Graph Neural Networks for Predicting Solvability of Finite Groups (2026)
  Tal Weissblat
  1.89
• CayleyPy-4: AI-Holography. Towards analogs of holographic string dualities for AI tasks (2026)
  A. Chervov et al.
  1.72
• Rethinking Diffusion Models with Symmetries through Canonicalization with Applications to Molecular Graph Generation (2026)
  Cai Zhou et al.
  1.67
• CayleyPy Growth: Efficient growth computations and hundreds of new conjectures on Cayley graphs (Brief version) (2025)
  A. Chervov et al.
  1.39
• The Generalized Skew Spectrum of Graphs (2025)
  Armando Bellante et al.
  1.17
• Diffusion Models for Cayley Graphs (2025)
  Michael R. Douglas and Kit Fraser-Taliente
  1.06
• CayleyPy RL: Pathfinding and Reinforcement Learning on Cayley Graphs (2025)
  A. Chervov et al.
  1.00
• Equivariant Maps for Hierarchical Structures (2020)
  Renhao Wang et al.
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• Learning Linear Symmetries in Data Using Moment Matching (2022)
  Colin Hagemeyer
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• On the Expressive Power of Geometric Graph Neural Networks (2023)
  Chaitanya K. Joshi et al.
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• SE(3) symmetry lets graph neural networks learn arterial velocity estimation from small datasets (2023)
  Julian Suk et al.
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• Fast, Expressive SE$(n)$ Equivariant Networks through Weight-Sharing in Position-Orientation Space (2023)
  Erik J Bekkers et al.
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• A novel approach to graph distinction through GENEOs and permutants (2024)
  Giovanni Bocchi and Massimo Ferri and Patrizio Frosini
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