Key papers

• Generalizing Perron--Frobenius theory and eigenvector-based centralities to networks with complex edge weights (2026)
  Yu Tian and Mason A. Porter and Lucas B\"ottcher
  1.89
• Spectral Graph Sparsification Preserves Representation Geometry in Graph Neural Networks (2026)
  Sanjukta Krishnagopal
  1.83
• SPECTRA: Spectral Domain-Aware Graph Generation for Imbalanced Molecular Property Regression (2025)
  Brenda Nogueira et al.
  1.50
• Rotation-Parameterized Graph Fractional Fourier Transform: Definition, Properties, and Optimal Filtering (2025)
  Feiyue Zhao et al.
  1.50
• Generative Myopia: Why Diffusion Models Fail at Structure (2025)
  Milad Siami
  1.50
• Grassmanian Interpolation of Low-Pass Graph Filters: Theory and Applications (2025)
  Anton Savostianov et al.
  1.44
• Convergence of graph Dirichlet energies and graph Laplacians on intersecting manifolds of varying dimensions (2025)
  Leon Bungert and Dejan Slep\v{c}ev
  1.39
• Multi-Community Spectral Clustering for Geometric Graphs (2025)
  Luiz Emilio Allem and Konstantin Avrachenkov and Carlos Hoppen and Hariprasad Manjunath and Lucas Siviero Sibemberg
  1.33
• Graph Alignment via Birkhoff Relaxation (2025)
  Sushil Mahavir Varma et al.
  1.06
• Cluster Synchronization via Graph Laplacian Eigenvectors (2025)
  Tobias Timofeyev et al.
  1.06
• Nonlinear Higher-Order Label Spreading (2020)
  Francesco Tudisco et al.
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• Spectral Evolution with Approximated Eigenvalue Trajectories for Link Prediction (2020)
  Miguel Romero et al.
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• Interpretation of Brain Morphology in Association to Alzheimer's Disease Dementia Classification Using Graph Convolutional Networks on Triangulated Meshes (2020)
  Emanuel A. Azcona et al.
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• Graph Embedding with Data Uncertainty (2020)
  Firas Laakom et al.
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• Higher-Order Spectral Clustering for Geometric Graphs (2020)
  Konstantin Avrachenkov et al.
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• Impact of signal-to-noise ratio and bandwidth on graph Laplacian spectrum from high-dimensional noisy point cloud (2020)
  Xiucai Ding and Hau-Tieng Wu
  —
• LDLE: Low Distortion Local Eigenmaps (2021)
  Dhruv Kohli et al.
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• Hypergraph Laplacians in Diffusion Framework (2021)
  Mehmet Emin Aktas and Esra Akbas
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• Large sample spectral analysis of graph-based multi-manifold clustering (2021)
  Nicolas Garcia Trillos et al.
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• A common variable minimax theorem for graphs (2021)
  Ronald R. Coifman et al.
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• Simplicial Convolutional Filters (2022)
  Maosheng Yang et al.
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• Geometric Scattering on Measure Spaces (2022)
  Joyce Chew and Matthew Hirn and Smita Krishnaswamy and Deanna Needell and Michael Perlmutter and Holly Steach and Siddharth Viswanath and Hau-Tieng Wu
  —
• Spreading and Structural Balance on Signed Networks (2022)
  Yu Tian and Renaud Lambiotte
  —
• Fundamental Limits of Spectral Clustering in Stochastic Block Models (2023)
  Anderson Ye Zhang
  —
• SHEEP: Signed Hamiltonian Eigenvector Embedding for Proximity (2023)
  Shazia'Ayn Babul et al.
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• Topology and spectral interconnectivities of higher-order multilayer networks (2023)
  Elka\"ioum M. Moutuou et al.
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• Fast Online Node Labeling for Very Large Graphs (2023)
  Baojian Zhou et al.
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• Analysis and Approximate Inference of Large Random Kronecker Graphs (2023)
  Zhenyu Liao et al.
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• Structural Balance and Random Walks on Complex Networks with Complex Weights (2023)
  Yu Tian et al.
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• Introduction to graph theory and basic algorithms (2023)
  Mikhail Tuzhilin et al.
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• A kernel-based analysis of Laplacian Eigenmaps (2024)
  Martin Wahl
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• Leveraging advances in machine learning for the robust classification and interpretation of networks (2024)
  Raima Carol Appaw et al.
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• On the Expressive Power of Spectral Invariant Graph Neural Networks (2024)
  Bohang Zhang et al.
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• Balance with Memory in Signed Networks via Mittag-Leffler Matrix Functions (2024)
  Yu Tian et al.
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• Graph Fourier Neural Kernels (G-FuNK): Learning Solutions of Nonlinear Diffusive Parametric PDEs on Multiple Domains (2024)
  Shane E. Loeffler et al.
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• Centrality Graph Shift Operators for Graph Neural Networks (2024)
  Yassine Abbahaddou et al.
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