Statistical and Nonlinear Physics papers
Pofolia’s corpus holds 64 papers from Statistical and Nonlinear Physics (2012–2026), each with a short summary. Below, the 20 most-cited, then the most recently added.
At a glance
- Most cited: Phase Transitions and Critical Phenomena (2016, 11,283 citations).
- The 20 papers listed have 81,904 citations between them.
- 10 of the 11 with a known journal quartile appeared in a Q1 journal.
- 7 have a free full text (open access).
- Most frequent journals: Proceedings of the National Academy of Sciences, Annual Review of Fluid Mechanics, IEEE Signal Processing Magazine.
- Published between 2012 and 2022.
Most cited
Ranked by citation count. Because citations accumulate over time, this list naturally leans towards work published a few years ago; for where the field is now, see “recently added”. How to read the signals
Phase Transitions and Critical Phenomena
2016 · FWCI 280.74 · 11,283 citations
This serial provides review articles on phase transitions and critical phenomena, aiming to serve as standard references for researchers and graduate students.
Power-law distributions in empirical data
INDIGO (University of Illinois at Chicago) · 2018 · FWCI 304.51 · 6,799 citations · Open access
This paper introduces improved statistical techniques for accurately identifying and characterizing power-law distributions in real-world data, overcoming limitations of standard methods like least-squares fitting.
Joint effect of ageing and multilayer structure prevents ordering in the voter model
LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · 2017 · FWCI 7.96 · 5,609 citations
A multilayer voter model with agent 'ageing' (decreasing update probability over time) exhibits a critical multiplexity (q*) where ordering to a consensus state is prevented below this threshold.
CRITICAL QUESTIONS FOR BIG DATA
Information Communication & Society · 2012 · Q1 · SJR 1.00 · FWCI 150.63 · 5,355 citations
This article argues for critical interrogation of Big Data's assumptions and biases, highlighting its potential for both societal benefit and harm.
From Louvain to Leiden: guaranteeing well-connected communities
Scientific Reports · 2019 · Q1 · FWCI 176.25 · 5,324 citations · Open access
The popular Louvain algorithm for network community detection can produce disconnected communities, with up to 16% disconnected in experiments; the new Leiden algorithm guarantees connected communities and converges to optimal partitions.
Discovering governing equations from data by sparse identification of nonlinear dynamical systems
Proceedings of the National Academy of Sciences · 2016 · Q1 · SJR 3.00 · FWCI 86.87 · 4,604 citations · Open access
A new algorithm uses sparsity-promoting techniques and machine learning to discover governing equations from noisy data, creating parsimonious models that balance accuracy and complexity.
The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains
IEEE Signal Processing Magazine · 2013 · Q1 · SJR 2.00 · FWCI 109.05 · 4,454 citations
Signal processing on graphs merges graph theory with harmonic analysis to process high-dimensional data residing on network structures. This field develops methods to adapt fundamental operations like filtering and downsampling to irregular graph domains.
From Louvain to Leiden: guaranteeing well-connected communities
Leiden Repository (Leiden University) · 2019 · FWCI 188.00 · 4,329 citations
A new algorithm, Leiden, guarantees connected communities in network analysis, unlike the widely-used Louvain algorithm which can produce disconnected communities.
Experimental evidence of massive-scale emotional contagion through social networks
Proceedings of the National Academy of Sciences · 2014 · Q1 · SJR 3.00 · FWCI 188.89 · 3,679 citations · Open access
Reducing the emotional content in Facebook's News Feed experimentally altered users' own emotional expressions. Less positive content led to fewer positive posts, while less negative content led to fewer negative posts.
Advanced Engineering Thermodynamics
2016 · FWCI 57.93 · 3,374 citations
This work presents a comprehensive textbook on advanced engineering thermodynamics, covering fundamental laws, exergy analysis, and applications across various systems and power generation methods.
Stochastic thermodynamics, fluctuation theorems and molecular machines
Reports on Progress in Physics · 2012 · Q1 · SJR 3.00 · FWCI 102.15 · 3,334 citations
Stochastic thermodynamics offers a framework to extend classical thermodynamic concepts like work and heat to individual non-equilibrium processes, applicable to systems like molecular motors and thermoelectric devices.
Multilayer networks
Journal of Complex Networks · 2014 · Q3 · FWCI 143.87 · 3,125 citations
This paper reviews the burgeoning field of multilayer networks, proposing a unified framework and terminology to consolidate research across disciplines. It aims to bridge disparate concepts like multiplex and interdependent networks.
ForceAtlas2, a Continuous Graph Layout Algorithm for Handy Network Visualization Designed for the Gephi Software
PLoS ONE · 2014 · Q1 · FWCI 82.76 · 2,968 citations · Open access
ForceAtlas2 is a new default layout algorithm for the Gephi network visualization software, designed as an all-around solution for typical user networks.
{SNAP Datasets}: {Stanford} Large Network Dataset Collection
2014 · FWCI 141.50 · 2,762 citations
This paper presents the SNAP Datasets, a collection of over 50 large network datasets with sizes ranging from thousands to tens of millions of nodes and edges.
Machine Learning for Fluid Mechanics
Annual Review of Fluid Mechanics · 2019 · Q1 · SJR 6.00 · FWCI 99.87 · 2,675 citations
Machine learning (ML) techniques are increasingly vital for extracting knowledge from the vast data generated in fluid mechanics, offering new ways to understand, model, and control fluid flows.
Citation-based clustering of publications using CitNetExplorer and VOSviewer
Scientometrics · 2017 · Q1 · SJR 1.00 · FWCI 37.84 · 2,604 citations · Open access
This paper demonstrates a method for clustering scientific publications using citation data, leveraging the freely available tools CitNetExplorer and VOSviewer.
The Network Data Repository with Interactive Graph Analytics and Visualization
Proceedings of the AAAI Conference on Artificial Intelligence · 2015 · SJR 1.00 · 2,430 citations
NetworkRepository (NR) is a novel web-based platform that allows users to download, interactively analyze, and visualize network data in real-time, differentiating itself from static repositories.
Lectures on Phase Transitions and the Renormalization Group
2018 · FWCI 23.49 · 2,407 citations
This book offers a clear, understanding-focused introduction to phase transitions and the renormalization group, suitable for graduate students and researchers in statistical mechanics.
Quantum Dissipative Systems
World Scientific Publishing Co. Pte. Ltd. eBooks · 2012 · FWCI 55.91 · 2,395 citations
This work surveys various theoretical approaches to understanding open quantum systems, focusing on methods like path integrals, influence functionals, and system-plus-reservoir models.
Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next
Journal of Scientific Computing · 2022 · Q1 · SJR 1.00 · FWCI 200.13 · 2,394 citations · Open access
Physics-Informed Neural Networks (PINNs) integrate model equations (like PDEs) into neural networks, enabling them to solve various differential equations and fit data simultaneously.
Recently added
Model Inversion Attacks Through Target-Specific Conditional Diffusion Models
ACM Transactions on Multimedia Computing Communications and Applications · 2026 · Q1 · FWCI 4.55 · 2 citations · Open access
A new method, Diff-MI, uses conditional diffusion models to reconstruct private training images from AI classifiers with 20% better fidelity (lower FID) than previous attacks.
Gibbs state preparation for commuting Hamiltonian: Mapping to classical Gibbs sampling
Quantum · 2026 · Q1 · SJR 2.00 · 1 citations
A new quantum algorithm efficiently prepares Gibbs states for commuting local Hamiltonians (CLHs) by reducing the problem to classical Gibbs sampling, achieving state-of-the-art results and enabling preparation in previously inaccessible low-temperature regimes.
Discrete quantum Gaussians and central limit theorem
Advances in Mathematics · 2026 · Q1 · SJR 2.00 · FWCI 4.69 · 4 citations
Stabilizer states in discrete-variable quantum systems are analogous to Gaussian states in continuous-variable systems, termed 'discrete quantum Gaussians'. Their convolution yields another stabilizer state, and they extremize quantum entropy and Fisher information.
A frame work to predict the influencing nodes in social networking platform using graph neural network model
Social Network Analysis and Mining · 2026 · Q1 · FWCI 406.24 · 85 citations
A novel Graph Neural Network (GNN) and Ant Colony Optimization (ACO) hybrid framework effectively identifies influential nodes for maximizing information diffusion in social networks, outperforming baseline methods in spread and offering comparable computational efficiency.
Analyzing Social Networks
2024 · FWCI 50.08 · 1,974 citations
This guide provides a practical, step-by-step approach to social network analysis, covering mathematical foundations, software tools (NeTDraw, UNICET), and the entire research process from data collection to analysis.
Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next
Journal of Scientific Computing · 2022 · Q1 · SJR 1.00 · FWCI 200.13 · 2,394 citations · Open access
Physics-Informed Neural Networks (PINNs) integrate model equations (like PDEs) into neural networks, enabling them to solve various differential equations and fit data simultaneously.
Physics-Informed Neural Networks for Heat Transfer Problems
Journal of Heat Transfer · 2021 · FWCI 71.32 · 1,202 citations
Physics-informed neural networks (PINNs) can solve complex heat transfer problems, including those with unknown boundary conditions and phase changes, by simultaneously fitting sparse data and enforcing physical laws via automatic differentiation.
Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks
SIAM Journal on Scientific Computing · 2021 · Q1 · SJR 1.00 · FWCI 84.54 · 1,697 citations
This paper identifies and quantifies pathological gradient flows in Physics-Informed Neural Networks (PINNs) that hinder training, particularly for stiff dynamics, and proposes a novel adaptive activation function that significantly improves convergence and accuracy.
Journals in this field
The journals that publish most of this field’s papers. Quartile (Q1–Q4), SJR and h-index are from SCImago Journal Rank; “in this field” is how many of the journal’s pooled papers belong here. What is a Q1 journal? · What is the h-index?
| Journal | Quartile | SJR | h-index | In this field | Summaries |
|---|---|---|---|---|---|
| Proceedings of the National Academy of Sciences of the United States of America | Q1 | 3.00 | 929 | 4 | 110 |
| Science | Q1 | 10.00 | 1433 | 2 | 268 |
| Annual Review of Fluid Mechanics | Q1 | 6.00 | 243 | 2 | 4 |
| IEEE Transactions on Knowledge and Data Engineering | Q1 | 3.00 | 233 | 2 | 4 |
| Nature Communications | Q1 | 4.00 | 634 | 1 | 94 |
| Science Advances | Q1 | 4.00 | 325 | 1 | 22 |
| Reports on Progress in Physics | Q1 | 3.00 | 273 | 1 | 7 |
| Advances in Mathematics | Q1 | 2.00 | 124 | 1 | 3 |
Every journal in Statistical and Nonlinear Physics (Q1–Q4) →
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