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Effects of relational graph modularity and depth on the learning performance of neural networks
Jul 14, 2025Yash Arya, Sang Hoon LeeNetwork Science Neural Network
Department of Physics, Gyeongsang National University, Jinju, 52828, Korea
Understanding Two-Layer Neural Networks with Smooth Activation Functions
Jul 11, 2025Changcun HuangTwo-Layer Neural Networks Activation Functions
Shuitu Institute of Applied Mathematics, Chongqing 400700, P.R.C
PERTINENCE: Input-based Opportunistic Neural Network Dynamic Execution
Jul 2, 2025Omkar Shende, Gayathri Ananthanarayanan, Marcello TraiolaNeural Network Inference Neural Network
Department of Computer Science and Engineering, Indian Institute of Technology Dharwad, Karnataka, India · University of Rennes, CNRS, Inria, IRISA, Rennes, France
Stochastic Quantum Spiking Neural Networks with Quantum Memory and Local Learning
Jun 26, 2025Jiechen Chen, Bipin Rajendran, Osvaldo SimeoneQuantum Neural Networks Spiking Neural Networks
Department of Engineering, King’s College London, London, WC2R 2LS, UK · Institute for Intelligent Networked Systems, Northeastern University London, One Portsoken Street, London, E1 8PH, UK
DPG loss functions for learning parameter-to-solution maps by neural networks
Jun 23, 2025Pablo Cortés Castillo, Wolfgang Dahmen, Jay GopalakrishnanVariational Formulation Neural Network
OD3: Optimization-free Dataset Distillation for Object Detection
Jun 2, 2025Salwa K. Al Khatib, Ahmed ElHagry, Shitong Shao +1Object Detection Diffusion-Based Dataset Distillation
Mohamed bin Zayed University of Artificial Intelligence · Hong Kong University of Science and Technology (Guangzhou)
Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks
May 26, 2025Safa Hamreras, Sukhbinder Singh, Román OrúsTensor Networks Neural Network
Donostia International Physics Center, Paseo Manuel de Lardizabal 4, E-20018 San Sebastián, Spain · Multiverse Computing, Spadina Ave., Toronto, ON M5T 2C2, Canada · Multiverse Computing, Paseo de Miramón 170, E-20014 San Sebastián, Spain
Sigma-Delta Neural Network Conversion on Loihi 2
May 9, 2025Matthew Brehove, Sadia Anjum Tumpa, Espoir Kyubwa +2Neuromorphic Computing Time-To-First-Spike
ChromoLogic LLC Monrovia, California, USA · Pennsylvania State University University Park, Pennsylvania, USA
A Theoretical Analysis of Provable Compositional Generalization in Neural Networks: A Necessary and Sufficient Condition
May 5, 2025Yuanpeng LiCompositional Generalization Neural Network
Deep learning with missing data
Apr 21, 2025Tianyi Ma, Tengyao Wang, Richard J. SamworthImputation Exponential Family
Statistical Laboratory, University of Cambridge · Department of Statistics, London School of Economics
Causal pieces: analysing and improving spiking neural networks piece by piece
Apr 18, 2025Dominik Dold, Philipp Christian PetersenSpiking Neural Networks Lfsr-Based Stochastic Leaky Integrate-And-Fire Neuron
FLASH: Flexible Learning of Adaptive Sampling from History in Temporal Graph Neural Networks
Apr 9, 2025Or Feldman, Krishna Sri Ipsit Mantri, Carola-Bibiane Schönlieb +2Temporal Graph Neural Networks Zero-Shot Link Prediction
Ben-Gurion University of the Negev, Israel · Purdue University, USA · University of Cambridge, UK
Hyperflux: Pruning Reveals Importance
Apr 6, 2025Eugen Barbulescu, Antonio Alexoaie, Lucian BusoniuUnstructured Pruning Structured Pruning
Department of Computer Science Technical University of Cluj-Napoca · Department of Automation Technical University of Cluj-Napoca
AYLA: Architecting a loss landscape in shallow neural networks to accelerate feature recovery
Apr 2, 2025Behnam Gheshlaghi, Shahin AtakishiyevAnisotropic Loss Landscapes Feature Learning
Traffic Engineering in Large-scale Networks with Generalizable Graph Neural Networks
Mar 31, 2025Fangtong Zhou, Xiaorui Liu, Ruozhou Yu +1Bringing Network Coding Accurate Traffic Forecasting
Department of Computer Science, North Carolina State University, Raleigh, NC, 27606, USA · School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, 85287, USA
PICID: Proof-Driven Clause Learning in Neural Network Verification
Mar 15, 2025Omri Isac, Idan Refaeli, Haoze Wu +2Neural Network Verification Theorem Proving
The Hebrew University of Jerusalem · Amherst College · Stanford University
Learning Constraints-Based Adaptive Hypergraph Neural Networks for Solving Vehicle Routing Problems
Mar 13, 2025Zhenwei Wang, Tiehua Zhang, Jing Liu +3Vehicle Routing Problem Combinatorial Optimization
University of Nottingham Ningbo China, 199 East Taikang Road, Ningbo, 315100, Zhejiang, China · Tongji University, 1239,Siping Road, Shanghai, 200092, Shanghai, China · State Key Laboratory for Novel Software Technology, Nanjing University, 163,Xianlin Road, Nanjing, 210023, Jiangsu, China +1
Fourier Multi-Component and Multi-Layer Neural Networks: Unlocking High-Frequency Potential
Feb 26, 2025Shijun Zhang, Hongkai Zhao, Yimin Zhong +1Two-Layer Neural Networks Neural Network
Department of Applied Mathematics Hong Kong Polytechnic University · Department of Mathematics Duke University · Department of Mathematics and Statistics Auburn University +1
Learning Chern Numbers of Topological Insulators with Gauge Equivariant Neural Networks
Feb 21, 2025Longde Huang, Oleksandr Balabanov, Hampus Linander +3Equivariant Neural Networks Quantum Phases
Department of Mathematical Sciences, Chalmers University of Technology and University of Gothenburg, SE-412 96 Gothenburg, Sweden. · Department of Physics, Stockholm University, AlbaNova University Center, SE-106 91 Stockholm, Sweden · VERSES AI Research Lab, Los Angeles, USA +1
Perforated Backpropagation: A Neuroscience Inspired Extension to Artificial Neural Networks
Jan 29, 2025Rorry Brenner, Laurent IttiNeurons Backpropagation
Perforated AI Inc. · Computer Science and Neuroscience, University of Southern California
Bridging Neural Networks and Wireless Systems with MIMO-OFDM Semantic Communications
Jan 28, 2025Hanju Yoo, Dongha Choi, Yonghwi Kim +4Orthogonal Frequency-Division Multiplexing Semantic Communications
School of Integrated Technology, Yonsei University, Seoul 03722, South Korea · Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA
Improving the adaptive and continuous learning capabilities of artificial neural networks: Lessons from multi-neuromodulatory dynamics
Jan 12, 2025Jie Mei, Alejandro Rodriguez-Garcia, Daigo Takeuchi +4Adaptive Deep Brain Stimulation Neural Network
IT:U Interdisciplinary Transformation University Austria, Linz, Austria · International Research Center for Neurointelligence, The University of Tokyo, Tokyo, Japan · Department of Anatomy, University of Quebec in Trois-Rivieres, Trois-Rivieres, QC, Canada +8
Network Dynamics-Based Framework for Understanding Deep Neural Networks
Jan 5, 2025Yuchen Lin, Yong Zhang, Sihan Feng +1Neural Network Deep Learning
Department of Physics, Xiamen University, Xiamen 361005, China · 2Lanzhou Center for Theoretical Physics, Lanzhou University, Lanzhou 730000, China · 3Shenzhen Angel FOF Management Co., Ltd., Shenzhen 518000, China
A Tunable Despeckling Neural Network Stabilized via Diffusion Equation
Nov 24, 2024Yi Ran, Zhichang Guo, Jia Li +3Iterative Denoising Methods Synthetic Aperture Radar
School of Mathematics, Harbin Institute of Technology, 150001 Harbin, China · Helmholtz Imaging, Deutsches Elektronen-Synchrotron DESY, 22607 Hamburg, Germany · Fachbereich Mathematik, Universität Hamburg, 20146 Hamburg, Germany
Koopman-informed recurrent neural networks
Oct 30, 2024Erik Lien Bolager, Ana Čukarska, Iryna Burak +2Recurrent Neural Networks Koopman Operator
Munich Data Science Institute · School of Computation, Information and Technology, Technical University of Munich, Germany · Interdisciplinary Center for Scientific Computing (IWR) +1
Statistical Properties of Deep Neural Networks with Dependent Data
Oct 14, 2024Chad BrownExponential Family Minimax Rate
Department of Economics, University of Colorado Boulder, 80309, USA.
Generation of Uncertainty-Aware High-Level Spatial Concepts in Factorized 3D Scene Graphs via Graph Neural Networks
Sep 18, 2024Jose Andres Millan-Romera, Muhammad Shaheer, Miguel Fernandez-Cortizas +33D Scene Graphs Orb-Slam2
Automation and Robotics Research Group, Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg · University of Amsterdam
A Generalization Bound for Nearly-Linear Networks
Jul 9, 2024Eugene GolikovGeneralization Bounds Nonlinearity
Chair of Statistical Field Theory École Polytechnique Fédérale de Lausanne (EPFL)
Over-parameterization and Adversarial Robustness in Neural Networks: An Overview and Empirical Analysis
Jun 14, 2024Srishti Gupta, Zhang Chen, Luca Demetrio +9Overparameterization Neural Network
Sapienza University of Rome, Rome, Italy. · University of Cagliari, Cagliari, Italy. · Northwestern Polytechnical University, Xi’An, China. +1
A Computational Tropical Geometry Framework for Neural Networks
May 30, 2024Paul Lezeau, Thomas Walker, Yueqi Cao +2Neural Network Polytopes
Department of Mathematics, Imperial College London, UK · London School of Geometry and Number Theory, UK · Department of Electrical and Computer Engineering, Rice University, USA +2
Adversarial Rademacher Complexity of Deep Neural Networks
Nov 27, 2022Jiancong Xiao, Yanbo Fan, Ruoyu Sun +1Adversarial Training Generalization Bounds
The Chinese University of Hong Kong, Shenzhen Shenzhen, China · Nanjing University Suzhou, China
Theory of the Frequency Principle for General Deep Neural Networks
Jun 21, 2019Tao Luo, Zheng Ma, Zhi-Qin John Xu +1Modern Deep Networks Neural Network
Department of Mathematics, Zheng Ma Purdue University · Department of Mathematics, Purdue University · New York University Abu Dhabi and Courant Institute

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Effects of relational graph modularity and depth on the learning performance of neural networks

Yash Arya, Sang Hoon Lee

Department of Physics, Gyeongsang National University, Jinju, 52828, Korea

Network Science Neural Network

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In recent years, graph-based machine learning techniques, such as reinforcement learning and graph neural networks, have garnered significant attention. While some recent studies have started to explore the relationship between the graph structure of neural networks and their predictive performance, they often limit themselves to a narrow range of model networks, particularly lacking mesoscale structures such as communities. Our work advances this area by conducting a more comprehensive investigation, incorporating realistic network structures characterized by heterogeneous degree distributions and community structures, which are typical characteristics of many real networks. These community structures offer a nuanced perspective on network architecture. Our analysis employs model networks such as random and scale-free networks, alongside a comparison with a biological neural network and its subsets for more detailed analysis. We examine the impact of these structural attributes on the performance of image classification tasks. Our findings reveal that structural properties do affect performance to some extent. Specifically, within moderate-depth architectures, networks featuring coherent, densely interconnected communities demonstrate enhanced learning capabilities. Crucially, we find that this advantage is strictly depth-dependent: extending the architecture to eight layers reverses the effect entirely. This comparison with the biological neural network emphasizes the relevance of our findings to real-world structures, suggesting an intriguing connection worth further exploration. This study contributes meaningfully to network science and machine learning, providing insights that could inspire the design of more biologically informed neural networks.

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