Publications

This page brings together publications from across the Erlangen AI Hub, showcasing research produced by Hub investigators, postdoctoral researchers, and PhD students. Outputs span the Hub’s research themes and reflect collaborative work across institutions and disciplines.

Selected publications are listed below and grouped by research theme to support exploration of the Hub’s research activity.

Understanding data

Understanding ML models

Understanding learning

Understanding decision-making

Other papers

Understanding data

Fibers of Point Cloud Persistence
David Beers, Heather A. Harrington, Jacob Leygonie, Uzu Lim, Louis Theran
https://arxiv.org/abs/2411.08201

Learning Inter-Atomic Potentials without Explicit Equivariance
Ahmed Elhag, Arun Raja, Alex Morehead, Samuel Blau, Garrett Morris, Michael Bronstein
https://arxiv.org/abs/2510.00027

Holes in Latent Space: Topological Signatures under Adversarial Influence
Aideen Fay, Inés García-Redondo, Qiquan Wang, Haim Dubossarsky, Anthea Monod, https://arxiv.org/abs/2505.20435
This work also contributes to Theme B.

Discrete Signature Tensors for Persistence Landscapes
Vincenzo Galgano, Heather A. Harrington, Daniel Tolosa
https://arxiv.org/abs/2505.02800

Confidence Bands for Multiparameter Persistence Landscapes
I. García-Redondo, A. Monod, Q. Wang
https://arxiv.org/abs/2504.01113
This work also contributes to Theme B.

Word Meanings in Transformer Language Models
Jumbly Grindrod, Peter Grindrod
https://arxiv.org/abs/2508.12863
This work also contributes to Theme B.

Dynamical Systems on Generalised Klein Bottles
P. Grindrod, K.M. Yim
https://doi.org/10.3390/e27020119

Modularity, Hierarchical Flows and Symmetry of the Drosophila Connectome
Peter Grindrod, Renaud Lambiotte, Rohit Sahasrabuddhe
https://arxiv.org/abs/2412.13202

Temporal Graph Learning Workshop
Shenyang Huang, Daniele Zambon, Andrea Cini, Farimah Poursafaei, Jacob Chmura, Julia Gastinger, Reihaneh Rabbany, Michael Bronstein
https://dl.acm.org/doi/abs/10.1145/3711896.3737855

Manifold Diffusion Geometry: Curvature, Tangent Spaces, and Dimension
Iolo Jones
https://arxiv.org/abs/2411.04100

Metric Flow Matching for Smooth Interpolations on the Data Manifold
Kacper Kapuściak, Peter Potaptchik, Teodora Reu, Leo Zhang, Alexander Tong, Michael Bronstein, Avishek Joey Bose, Francesco Di Giovanni
https://openreview.net/pdf/5fd21860d4ce3d4b1c290c2ac4b4e68d2b82e3a7.pdf
This work also contributes to Theme B.

Tropical Fréchet Means
Lin B., Ferry K., Améndola C., Monod A., Yoshida R.
https://arxiv.org/abs/2502.05322

Signature Methods in Machine Learning
Terry Lyons, Andrew D. McLeod
https://arxiv.org/abs/2206.14674
This work also contributes to Theme B.

Hippocampal Ripple Diversity Organises Neuronal Reactivation Dynamics in the Offline Brain
Manfredi Castelli, Vítor Lopes-dos-Santos, Giuseppe P. Gava, Renaud Lambiotte, David Dupret
https://www.biorxiv.org/content/10.1101/2025.03.11.642571v2

Detection of Anomalous Spatio-Temporal Patterns of App Traffic in Response to Catastrophic Events
S. Medina, S.A. Babul, R. Sahasrabuddhe, T. LaRock, R. Lambiotte, N. Pedreschi
https://arxiv.org/abs/2409.01355

Persistent (Co)Homology in Matrix Multiplication Time
Dmitriy Morozov, Primož Skraba
https://arxiv.org/abs/2412.02591

From Reductionism to Realism: Holistic Mathematical Modelling for Complex Biological Systems
Ramón Nartallo-Kaluarachchi, Renaud Lambiotte, Alain Goriely
https://arxiv.org/abs/2503.20511

Nonequilibrium Physics of Brain Dynamics
Ramón Nartallo-Kaluarachchi, Morten L. Kringelbach, Gustavo Deco, Renaud Lambiotte, Alain Goriely
https://arxiv.org/abs/2504.12188

Cover Learning for Large-Scale Topology Representation
Luis Scoccola, Uzu Lim, Heather Harrington
https://arxiv.org/abs/2503.09767
This work also contributes to Theme B.

Garbage in, Garbage Out: Impacts of Data Quality on Criminal Network Intervention
Wang Ngai Yeung, Riccardo Di Clemente, Renaud Lambiotte
https://arxiv.org/abs/2501.01508

Entropy-Based Models to Randomize Real-World Hypergraphs
Fabio Saracco, Giovanni Petri, Renaud Lambiotte, Tiziano Squartini
https://arxiv.org/abs/2207.12123

Nondimensionalization is more science than art
Richard Tanburn, Danny Hendron, Philip Maini, Silviana Amethyst, Emilie Dufresne, Heather A. Harrington
https://arxiv.org/abs/2512.13455

On the Limitations of Fractal Dimension as a Measure of Generalization
Charlie B. Tan, Inés García-Redondo, Qiquan Wang, Michael M. Bronstein, Anthea Monod
https://openreview.net/pdf?id=YO6GVPUrKN
This work also contributes to Theme B.

On the Neural Feature Ansatz for Deep Neural Networks
Edward Tansley, Estelle Massart, Coralia Cartis
https://arxiv.org/abs/2510.15563

A Topological Gaussian Mixture Model for Bone Marrow Morphology in Leukaemia
Q. Wang, A. Song, A. Batsivari, D. Bonnet, A. Monod
https://arxiv.org/abs/2408.13685

Planner Aware Path Learning in Diffusion Language Models Training
Fred Zhangzhi Peng, Zachary Bezemek, Jarrid Rector-Brooks, Shuibai Zhang, Anru R. Zhang, Michael Bronstein, Avishek Joey Bose, Alexander Tong
https://arxiv.org/abs/2509.23405

Understanding ML models

Policy Verification in Stochastic Dynamical Systems Using Logarithmic Neural Certificates
Thom Badings, Wietze Koops, Sebastian Junges, Nils Jansen
https://link.springer.com/chapter/10.1007/978-3-031-98679-6_16

Homomorphism Counts as Structural Encodings for Graph Learning (ICLR)
Linus Bao, Emily Jin, Michael Bronstein, Ismail Ilkan Ceylan, Matthias Lanzinger
https://arxiv.org/pdf/2410.18676

Fisher Flow Matching for Generative Modelling over Discrete Data
Oscar Davis, Samuel Kessler, Mircea Petrache, İsmail İlkan Ceylan, Michael Bronstein, Avishek Joey Bose
https://openreview.net/pdf/c16ab010bac44658a3c695f87e0c8d925deca3d4.pdf

Relaxed Equivariance via Multitask Learning
Ahmed A. Elhag, T. Konstantin Rusch, Francesco Di Giovanni, Michael Bronstein
https://arxiv.org/abs/2410.17878

Confidence Bands for Multiparameter Persistence Landscapes
I. García-Redondo, A. Monod, Q. Wang
https://arxiv.org/abs/2504.01113
This work also contributes to Theme A.

GradMetaNet: An Equivariant Architecture for Learning on Gradients
Yoav Gelberg, Yam Eitan, Aviv Navon, Aviv Shamsian, Theo (Moe)Putterman, Michael Bronstein, Haggai Maron
https://arxiv.org/abs/2507.01649

Word Meanings in Transformer Language Models
Jumbly Grindrod, Peter Grindrod
https://arxiv.org/abs/2508.12863
This work also contributes to Theme A.

Signature Methods in Machine Learning
Terry Lyons, Andrew D. McLeod
https://arxiv.org/abs/2206.14674
This work also contributes to Theme A.

Future Directions in the Theory of Graph Machine Learning
Christopher Morris, Fabrizio Frasca, Nadav Dym, Haggai Maron, Ismail Ilkan Ceylan, Ron Levie, Derek Lim, Michael Bronstein, Martin Grohe, Stefanie Jegelka
https://openreview.net/pdf?id=wBr5ozDEKp

Mind the Gap: A Spectral Analysis of Rank Collapse and Signal Propagation in Attention Layers
Alireza Naderi, Thiziri Nait Saada, Jared Tanner
https://arxiv.org/abs/2410.07799

Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction
Jarrid Rector-Brooks, Mohsin Hasan, Zhangzhi Peng, Zachary Quinn, Chenghao Liu, Sarthak Mittal, Nouha Dziri, Michael Bronstein, Yoshua Bengio, Pranam Chatterjee, Alexander Tong, Avishek Joey Bose
https://arxiv.org/abs/2410.08134

FORT: Forward-Only Regression Training of Normalizing Flows
Danyal Rehman, Oscar Davis, Jiarui Lu, Jian Tang, Michael Bronstein, Yoshua Bengio, Alexander Tong, Avishek Joey Bose
https://arxiv.org/abs/2506.01158

Cover Learning for Large-Scale Topology Representation
Luis Scoccola, Uzu Lim, Heather Harrington
https://arxiv.org/abs/2503.09767
This work also contributes to Theme A.

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality
Joshua Southern, Yam Eitan, Guy Bar-Shalom, Michael M. Bronstein, Haggai Maron, Fabrizio Frasca
https://arxiv.org/pdf/2501.03113

Understanding learning

None recorded

Understanding decision-making

None recorded

Other papers

Semantically Labelled Automata for Multi-Task Reinforcement Learning with LTL Instructions
Alessandro Abate, Giuseppe De Giacomo, Mathias Jackermeier, Jan Kretinsky, Maximilian Prokop, and Christoph Weinhuber
https://arxiv.org/pdf/2602.06746

Best-Effort Policies for Robust Markov Decision Processes
Alessandro Abate, Thom Badings, Giuseppe De Giacomo, Francesco Fabiano
https://arxiv.org/abs/2508.07790

Individualised Counterfactual Examples Using Conformal Prediction Intervals
James M. Adams, Gesine Reinert, Lukasz Szpruch, Carsten Maple, Andrew Elliott
https://arxiv.org/abs/2505.22326

LTLf+ and PPLTL+: Extending LTLf and PPLTL+ to Infinite Traces
Benjamin Aminof, Giuseppe De Giacomo, Sasha Rubin, Moshe Y. Vardi
https://arxiv.org/abs/2411.09366

Strong and Weak Random Walks on Signed Networks
S. A. Babul, Y. Tian, R. Lambiotte
https://www.nature.com/articles/s44260-025-00027-1

Probabilistic Alternating Simulations for Policy Synthesis in Uncertain Stochastic Dynamical Systems
Thom Badings, Alessandro Abate
https://arxiv.org/abs/2508.05062

Carré du champ Flow Matching: Better Quality–Generalisation Trade-off in Generative Models
Jacob Bamberger, Iolo Jones, Dennis Duncan, Michael M. Bronstein, Pierre Vandergheynst, Adam Gosztolai
https://arxiv.org/abs/2510.05930

Homomorphism Counts as Structural Encodings for Graph Learning (ICLR)
Linus Bao, Emily Jin, Michael Bronstein, Ismail Ilkan Ceylan, Matthias Lanzinger
https://arxiv.org/pdf/2410.18676

Signature Methods in Finance: An Introduction with Computational Applications
Christian Bayer, Gonçalo dos Reis, Blanka Horváth
https://link.springer.com/book/10.1007/978-3-031-97239-3

The Tropical Galaxy of a Laman Graph
Amelia Bielby, Arushi Chauhan, Cassia Pearce, Yue Ren
https://arxiv.org/abs/2511.09246

gLSTM: Mitigating Over-Squashing by Increasing Storage Capacity
Hugh Blayney, Álvaro Arroyo, Xiaowen Dong, Michael M. Bronstein
https://arxiv.org/abs/2510.08450

Scaling Multi-Agent Epistemic Planning through GNN-Derived Heuristics
Giovanni Briglia, Francesco Fabiano, Stefano Mariani
https://arxiv.org/abs/2508.12840

Foundations of Differential Calculus for Modules over Posets
J. Brodzki, R. Levi, H. Riihimäki
https://arxiv.org/abs/2307.02444v4

Computing the Tropical Abel–Jacobi Transform and Tropical Distances for Metric Graphs
Yueqi Cao, Anthea Monod
https://arxiv.org/abs/2504.11619

On Global Rates for Regularization Methods Based on Secant Derivative Approximations
Coralia Cartis, Sadok Jerad
https://arxiv.org/abs/2509.07580

Topological Data Analysis of Abelian Magnetic Monopoles in Gauge Theories
X. Crean, J. Giansiracusa, B. Lucini
https://arxiv.org/abs/2501.19320

Simplicity of confinement in SU(3) Yang-Mills theory
Xavier Crean, Jeffrey Giansiracusa, and Biagio Lucini
https://arxiv.org/pdf/2602.10088

Tropical Methods for Building Real Space Sextics with Totally Real Tritangent Planes
Maria Angelica Cueto, Yoav Len, Hannah Markwig, Yue Ren
https://arxiv.org/abs/2512.24277

Topological Data Analysis of Abelian Magnetic Monopoles in Gauge Theories
X. Crean, J. Giansiracusa, B. Lucini
https://arxiv.org/abs/2501.19320

Generalised Flow Maps for Few-Step Generative Modelling on Riemannian Manifolds
Oscar Davis, Michael S. Albergo, Nicholas M. Boffi, Michael M. Bronstein, Avishek Joey Bose
https://arxiv.org/html/2510.21608v1

TGM: A Modular Framework for Machine Learning on Temporal Graphs
Jacob Chmura, Shenyang Huang, Ali Parviz, Farimah Poursafaei, Michael M. Bronstein, Guillaume Rabusseau, Matthias Fey, Reihaneh Rabbany
https://openreview.net/pdf?id=7Bzm7GrP4d

TGM: A Modular and Efficient Library for Machine Learning on Temporal Graphs
Jacob Chmura, Shenyang Huang, Tran Gia Bao Ngo, Ali Parviz, Farimah Poursafaei, Jure Leskovec, Michael Bronstein, Guillaume Rabusseau, Matthias Fey, Reihaneh Rabbany
https://arxiv.org/abs/2510.07586

How Controlling the Variance can Improve Training Stability of Sparsely Activated DNNs and CNNs
Emily Dent, Jared Tanner
https://arxiv.org/pdf/2602.05779

Gremban Expansion for Signed Networks: Algebraic and Combinatorial Foundations for Community–Faction Detection
Fernando Díaz-Díaz, Karel Devriendt, Renaud Lambiotte
https://www.researchgate.net/publication/395582839

Relaxed Equivariance via Multitask Learning
Ahmed A. Elhag, T. Konstantin Rusch, Francesco Di Giovanni, Michael Bronstein
https://arxiv.org/abs/2410.17878

Mean-Field Approximation for Networks with Synchrony-Driven Adaptive Coupling
N. Fennelly, A. Neff, R. Lambiotte, A. Keane, Á. Byrne
https://pubs.aip.org/aip/cha/article/35/1/013152/3332426

Projective Hypersurfaces in Tropical Scheme Theory I: The Macaulay Ideal
Alex Fink, Jeffrey Giansiracusa, Noah Giansiracusa, Joshua Mundinger
https://arxiv.org/abs/2405.16338

Discrete Signature Tensors for Persistence Landscapes
Vincenzo Galgano, Heather A. Harrington, Daniel Tolosa
https://arxiv.org/abs/2505.02800

Global Synchronization in Matrix-Weighted Networks
Anna Gallo, Yu Tian, Renaud Lambiotte, Timoteo Carletti
https://arxiv.org/abs/2507.12322

Solving MDPs with LTLf+ and PPLTL+ Temporal Objectives
Giuseppe De Giacomo, Yong Li, Sven Schewe, Christoph Weinhuber, Pian Yu
https://arxiv.org/abs/2505.17264

Computational Grounding of Responsibility Attribution and Anticipation in LTLf
Giuseppe De Giacomo, Emiliano Lorini, Timothy Parker, Gianmarco Parretti
https://arxiv.org/abs/2410.14544

Modularity, Hierarchical Flows and Symmetry of the Drosophila Connectome
Peter Grindrod, Renaud Lambiotte, Rohit Sahasrabuddhe
https://arxiv.org/abs/2412.13202

Dynamical Systems on Generalised Klein Bottles
Peter Grindrod, K. M. Yim
https://doi.org/10.3390/e27020119

On the Next Generation for Neuromorphic Computing and Neuromorphic AI
Peter Grindrod
https://www.researchgate.net/publication/393280208

Resilience, Tipping Points, and Hysteresis
Peter Grindrod
https://www.researchgate.net/publication/400576811_Resilience_Tipping_Points_and_Hysteresis

Emergent Photons and Mechanisms of Confinement
Jeffrey Giansiracusa, David Lanners, Tin Sulejmanpasic
https://arxiv.org/abs/2505.00079

Learning Iterated Function Systems from Time Series of Partial Observations
Emilia Gibson, Jeroen S. W. Lamb
https://arxiv.org/abs/2508.13794

GradMetaNet: An Equivariant Architecture for Learning on Gradients
Yoav Gelberg, Yam Eitan, Aviv Navon, Aviv Shamsian, Theo (Moe) Putterman, Michael Bronstein, Haggai Maron
https://arxiv.org/abs/2507.01649

Coarse-Graining Nonequilibrium Diffusions with Markov Chains
Alain Goriely, Ramón Nartallo-Kaluarachchi, Renaud Lambiotte
https://arxiv.org/abs/2511.05366

Manipulating Collective Opinion through Social Network Intervention
Shigefumi Hata, Hiroya Nakao, Ryota Kobayashi
https://doi.org/10.48550/arXiv.2511.12444

Emerson–Lei and Manna–Pnueli Games for LTLf+ and PPLTL+ Synthesis
Daniel Hausmann, Shufang Zhu, Gianmarco Parretti, Christoph Weinhuber, Giuseppe De Giacomo, Nir Piterman
https://arxiv.org/abs/2508.14725

Of Graphs and Tables: Zero-Shot Node Classification with Tabular Foundation Models
Adrian Hayler, Xingyue Huang, Ismail Ilkan Ceylan, Michael Bronstein, Ben Finkelshtein
https://arxiv.org/abs/2509.07143

Coarse-Graining Directed Networks with Ergodic Sets Preserving Diffusive Dynamics
E. Hörmann, R. Lambiotte
https://arxiv.org/abs/2503.18823

Equitability and Explosive Synchronisation in Multiplex and Higher-Order Networks
Kirill Kovalenko, Gonzalo Contreras-Aso, Charo I. del Genio, Stefano Boccaletti, Rubén Sánchez-García
https://arxiv.org/abs/2507.09319

Flock: A Knowledge Graph Foundation Model via Learning on Random Walks
Jinwoo Kim, Xingyue Huang, Krzysztof Olejniczak, Kyungbin Min, Michael Bronstein, Seunghoon Hong, İsmail İlkan Ceylan
https://arxiv.org/abs/2510.01510

Complex-Weighted Convolutional Networks: Provable Expressiveness via Complex Diffusion
Cristina López Amado, Tassilo Schwarz, Yu Tian, Renaud Lambiotte
https://openreview.net/forum?id=EgRvGMd2Zp

Signature Methods in Machine Learning
Terry Lyons, Andrew D. McLeod
https://arxiv.org/abs/2206.14674

Flow-Based Fragment Identification via Contrastive Learning of Binding Site-Specific Latent Representations
Rebecca Manuela Neeser, Ilia Igashov, Arne Schneuing, Michael M. Bronstein
https://openreview.net/pdf/0e5806df93426cbba7273ee4c3b2eb94545140.pdf

Flow-Based Fragment Identification via Binding Site-Specific Latent Representations
Rebecca Manuela Neeser, Ilia Igashov, Arne Schneuing, Michael Bronstein, Philippe Schwaller, Bruno Correia
https://arxiv.org/abs/2509.13216

Connected Causal Graphs for Real-World Science
Amine M’Charrak, Abbavaram Gowtham Reddy, Thomas Lukasiewicz, Michael M. Bronstein, Krikamol Muandet
https://openreview.net/forum?id=i0rFGQBGzs

Riemannian Neural Optimal Transport
Alessandro Micheli, Yueqi Cao, Anthea Monod, Samir Bhatt
https://arxiv.org/abs/2602.03566

Topological Model Selection: A Case-Study in Tumour-Induced Angiogenesis
Robert A. McDonald, Helen M. Byrne, Heather A. Harrington, Thomas Thorne, Bernadette J. Stolz
https://arxiv.org/abs/2504.15442

Higher-Order Lipschitz Sandwich Theorems
Andrew D. McLeod, Terry Lyons
https://arxiv.org/abs/2404.06849

A Topological Approach to the Cahn–Hilliard Equation and Hyperuniform Fields
Abel H. G. Milon, Otto Sumray, Heather A. Harrington, Axel Voigt, Marco Salvalaglio
https://arxiv.org/abs/2509.05339

Data-Driven Abstraction and Synthesis for Stochastic Systems with Unknown Dynamics
Mahdi Nazeri, Thom Badings, Anne-Kathrin Schmuck, Sadegh Soudjani, Alessandro Abate
https://arxiv.org/abs/2508.15543

LLMs Can Hide Text in Other Text of the Same Length
Antonio Norelli, Michael Bronstein
https://arxiv.org/abs/2510.20075v1

Triadic Structures in Multislice Networks
K. Ren, T. Trauthwein, G. Reinert
https://arxiv.org/abs/2504.00508

Breaking Symmetry Bottlenecks in GNN Readouts
Mouad Talhi, Arne Wolf, Anthea Monod
https://arxiv.org/abs/2602.05950

Efficient Solution and Learning of Robust Factored MDPs
Yannik Schnitzer, Alessandro Abate, David Parker
https://doi.org/10.48550/arXiv.2508.00707

Probabilistic Performance Guarantees for Multi-Task Reinforcement Learning
Yannik Schnitzer, Mathias Jackermeier, Alessandro Abate, David Parker
https://arxiv.org/pdf/2602.02098

Certifiably Robust Policies for Uncertain Parametric Environments
Yannik Schnitzer, Alessandro Abate, David Parker
https://arxiv.org/abs/2408.03093

Cover Learning for Large-Scale Topology Representation
Luis Scoccola, Uzu Lim, Heather Harrington
https://arxiv.org/abs/2503.09767

Advancing Sustainable Development through AIxSDGs
David S. Steingard, Francesco Fabiano
Link (publisher chapter)

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality
Joshua Southern, Yam Eitan, Guy Bar-Shalom, Michael Bronstein, Haggai Maron, Fabrizio Frasca
https://arxiv.org/pdf/2501.03113

Scalable Equilibrium Sampling with Sequential Boltzmann Generators
C.B. Tan, A. Bose, C. Lin, K. Klein, Michael Bronstein, A. Tong
https://arxiv.org/abs/2502.18462

On the Neural Feature Ansatz for Deep Neural Networks
Edward Tansley, Estelle Massart, Coralia Cartis
https://arxiv.org/abs/2510.15563v1

Scalable Verification of Neural Control Barrier Functions Using Linear Bound Propagation
Nikolaus Vertovec, Frederik Baymler Mathiesen, Thom Badings, Luca Laurenti, Alessandro Abate
https://arxiv.org/abs/2511.06341

Persistent (Co)Homology in Matrix Multiplication Time
Dmitriy Morozov, Primož Skraba
https://arxiv.org/abs/2412.02591

Global Optimality Characterizations and Algorithms for Minimizing Quartically-Regularized Third-Order Taylor Polynomials
Wenqi Zhu, Coralia Cartis
https://arxiv.org/abs/2504.20259

Good-for-MDP State Reduction for Stochastic LTL Planning
Christoph Weinhuber, Giuseppe De Giacomo, Yong Li, Sven Schewe, Qiyi Tang
https://arxiv.org/abs/2511.09073

Louvain for Signed Networks
Pougué-Biyong, J. N., R. Lambiotte
https://arxiv.org/abs/2407.19288

Concise Network Models of Memory Dynamics Reveal Explainable Patterns in Path Data
Rohit Sahasrabuddhe, Renaud Lambiotte, Martin Rosvall
https://arxiv.org/abs/2501.08302

Quantifying Digital Habits
Matthew Sharpe, Michael Bowen, Renaud Lambiotte
https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-025-00581-7

Matrix-Weighted Networks for Modeling Multidimensional Dynamics
Yu Tian, S. Kojaku, H. Sayama, R. Lambiotte
https://arxiv.org/abs/2410.05188

Repairing a Failed Clustered Network by External Activation
Xun Zhou, Gaogao Dong, Fan Wang, Ruijin Du, Renaud Lambiotte
https://www.sciencedirect.com/science/article/abs/pii/S0960077925009099

On Complex Network Techniques for Atmospheric Flow Analysis: A Polar Vortex Case Study
María Reboredo Prado, Renaud Lambiotte, Irene Moroz, Scott Osprey
https://iopscience.iop.org/article/10.1088/2632-072X/ae20e9

Tropical Expressivity of Neural Networks
P. Lezeau, T. Walker, Y. Cao, S. Bhatia, Anthea Monod
https://arxiv.org/abs/2405.20174

On the Limitations of Fractal Dimension as a Measure of Generalization
C. B. Tan, Inés García-Redondo, Qiquan Wang, Michael M. Bronstein, Anthea Monod
https://openreview.net/pdf?id=YO6GVPUrKN

Mind the Gap: A Spectral Analysis of Rank Collapse and Signal Propagation in Attention Layers
Alireza Naderi, Thiziri Nait Saada, Jared Tanner
https://arxiv.org/abs/2410.07799

Permutation Equivariant Neural Networks for Symmetric Tensors
Edward Pearce-Crump
https://arxiv.org/abs/2503.11276

On Complex Network Techniques for Atmospheric Flow Analysis: A Polar Vortex Case Study
María Reboredo Prado, Renaud Lambiotte, Iván Moroz, Simon Osprey
https://doi.org/10.1088/2632-072X/ae20e9

Topology Across Scales on Heterogeneous Cell Data
Maria Torras-Pérez, Iris H. R. Yoon, Praveen Weeratunga, Ling-Pei Ho, Helen M. Byrne, Ulrike Tillmann, Heather A. Harrington
https://arxiv.org/abs/2505.02717

Graph Models of Brain State in Deep Anaesthesia Reveal Sink State Dynamics of Reduced Spatiotemporal Complexity
James Wilsenach, Charlotte M. Deane, Gesine Reinert, Katie Warnaby
https://direct.mit.edu/netn/article/doi/10.1162/netn.a.27/131738/

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