Key papers

• Exponential Capacity in Multilayer Hetero-Associative Neural Networks (2026)
  Elena Agliari et al.
  1.94
• Escaping Mode Collapse in LLM Generation via Geometric Regulation (2026)
  Xin Du et al.
  1.83
• Diffusion Operator Geometry of Feedforward Representations (2026)
  Kanishka Reddy
  1.83
• Sharp feature-learning transitions and Bayes-optimal neural scaling laws in extensive-width networks (2026)
  Minh-Toan Nguyen et al.
  1.83
• Local network evolution rules drive shortest path multiplicity (2026)
  Alexei Vazquez
  1.83
• Distinct mechanisms underlying in-context learning in transformers (2026)
  Cole Gibson et al.
  1.78
• Graphs are focal hypergraphs: strict containment in higher-order interaction dynamics (2026)
  Elka\"ioum M. Moutuou
  1.72
• Predictive Coding Graphs are a Superset of Feedforward Neural Networks (2026)
  Bj\"orn van Zwol
  1.72
• Benchmarking Graph Neural Networks in Solving Hard Constraint Satisfaction Problems (2026)
  Geri Skenderi et al.
  1.67
• On the origin of neural scaling laws: from random graphs to natural language (2026)
  Maissam Barkeshli et al.
  1.61
• Photonics-Enhanced Graph Convolutional Networks (2025)
  Yuan Wang et al.
  1.56
• Computing frustration and near-monotonicity in deep neural networks (2025)
  Joel Wendin et al.
  1.44
• Fisher Information, Training and Bias in Fourier Regression Models (2025)
  Lorenzo Pastori et al.
  1.44
• Graphical model for factorization and completion of relatively high rank tensors by sparse sampling (2025)
  Angelo Giorgio Cavaliere et al.
  1.44
• Inference in Spreading Processes with Neural-Network Priors (2025)
  Davide Ghio et al.
  1.39
• Limits of message passing for node classification: How class-bottlenecks restrict signal-to-noise ratio (2025)
  Jonathan Rubin et al.
  1.33
• A Geometric Perspective on the Difficulties of Learning GNN-based SAT Solvers (2025)
  Geri Skenderi
  1.33
• Graph Neural Network Approach to Predicting Magnetization in Quasi-One-Dimensional Ising Systems (2025)
  V. Slavin et al.
  1.28
• Graph Learning Metallic Glass Discovery from Wikipedia (2025)
  K.-C. Ouyang et al.
  1.28
• How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? (2025)
  Michela Lapenna et al.
  1.22
• Evolutionary chemical learning in dimerization networks (2025)
  Alexei V. Tkachenko et al.
  1.22
• Statistical physics analysis of graph neural networks: Approaching optimality in the contextual stochastic block model (2025)
  O. Duranthon et al.
  1.06
• Evidence of Replica Symmetry Breaking under the Nishimori conditions in epidemic inference on graphs (2025)
  Alfredo Braunstein et al.
  1.00
• In-situ graph reasoning and knowledge expansion using Graph-PReFLexOR (2025)
  Markus J. Buehler
  0.94
• A theoretical framework for overfitting in energy-based modeling (2025)
  Giovanni Catania et al.
  0.94
• Graph Neural Networks Do Not Always Oversmooth (2024)
  Bastian Epping et al.
  0.67
• Statistical inference of assortative community structures (2020)
  Lizhi Zhang and Tiago P. Peixoto
  —
• Path Integral Based Convolution and Pooling for Graph Neural Networks (2020)
  Zheng Ma et al.
  —
• Ollivier-Ricci curvature convergence in random geometric graphs (2020)
  Pim van der Hoorn et al.
  —
• Fractal Gaussian Networks: A sparse random graph model based on Gaussian Multiplicative Chaos (2020)
  Subhroshekhar Ghosh et al.
  —
• Unraveling the effects of multiscale network entanglement on disintegration of empirical systems (2020)
  Arsham Ghavasieh et al.
  —
• Non-normality and non-monotonic dynamics in complex reaction networks (2020)
  Zachary G. Nicolaou et al.
  —
• Disordered complex networks: energy optimal lattices and persistent homology (2020)
  Subhro Ghosh et al.
  —
• Machine Learning Link Inference of Noisy Delay-coupled Networks with Opto-Electronic Experimental Tests (2020)
  Amitava Banerjee et al.
  —
• Being Central on the Cheap: Stability in Heterogeneous Multiagent Centrality Games (2020)
  Gabriel Istrate and Cosmin Bonchi\c{s}
  —
• Symmetry-adapted graph neural networks for constructing molecular dynamics force fields (2021)
  Zun Wang et al.
  —
• Minimum Long-Loop Feedback Vertex Set and Network Dismantling (2021)
  Tianyi Li et al.
  —
• Epidemic Spreading and Digital Contact Tracing: Effects of Heterogeneous Mixing and Quarantine Failures (2021)
  Abbas K. Rizi et al.
  —
• Detecting Hidden Units and Network Size from Perceptible Dynamics (2021)
  Hauke Haehne et al.
  —
• Meta-validation of bipartite network projections (2021)
  Giulio Cimini et al.
  —
• The topological Dirac equation of networks and simplicial complexes (2021)
  Ginestra Bianconi
  —
• An Analytical Theory of Curriculum Learning in Teacher-Student Networks (2021)
  Luca Saglietti et al.
  —
• Regularization of Mixture Models for Robust Principal Graph Learning (2021)
  Tony Bonnaire et al.
  —
• Fluctuating ecological networks: a synthesis of maximum-entropy approaches for pattern detection and process inference (2021)
  Tancredi Caruso et al.
  —
• Combinatorial Optimization with Physics-Inspired Graph Neural Networks (2021)
  Martin J. A. Schuetz et al.
  —
• Directed Percolation in Temporal Networks (2021)
  Arash Badie-Modiri et al.
  —
• Dirac synchronization is rhythmic and explosive (2021)
  Lucille Calmon et al.
  —
• Spectral Detection of Simplicial Communities via Hodge Laplacians (2021)
  Sanjukta Krishnagopal et al.
  —
• Heterogeneous relational message passing networks for molecular dynamics simulations (2021)
  Zun Wang et al.
  —
• The physics of higher-order interactions in complex systems (2021)
  Federico Battiston et al.
  —
• Directed Percolation in Random Temporal Network Models with Heterogeneities (2021)
  Arash Badie-Modiri et al.
  —
• Deep learning via message passing algorithms based on belief propagation (2021)
  Carlo Lucibello et al.
  —
• Statistical physics of exchangeable sparse simple networks, multiplex networks and simplicial complexes (2021)
  Ginestra Bianconi
  —
• Structure-based approach to identifying small sets of driver nodes in biological networks (2021)
  Eli Newby et al.
  —
• Quantum Link Prediction in Complex Networks (2021)
  Jo\~ao P. Moutinho et al.
  —
• Quantum Capsule Networks (2022)
  Zidu Liu et al.
  —
• Network-based link prediction of scientific concepts -- a Science4Cast competition entry (2022)
  Joao P. Moutinho et al.
  —
• Graph Coloring with Physics-Inspired Graph Neural Networks (2022)
  Martin J. A. Schuetz et al.
  —
• Recurrent Spectral Network (RSN): shaping the basin of attraction of a discrete map to reach automated classification (2022)
  Lorenzo Chicchi et al.
  —
• Spin-Dependent Graph Neural Network Potential for Magnetic Materials (2022)
  Hongyu Yu et al.
  —