Article
Physics, Fluids & Plasmas
Yating Wang, Hanshuang Chen
Summary: In this paper, the authors investigate the effects of stochastic resetting on the entropy rate of discrete-time Markovian processes. The study reveals nontrivial and interesting features of stochastic dynamics, showing a nonmonotonic dependence of the entropy rate on the resetting probability. The research also explores the mixing properties of stochastic processes on different network topologies.
Article
Physics, Fluids & Plasmas
Hanshuang Chen, Yanfei Ye
Summary: This study investigates discrete-time random walks on networks subject to time-dependent stochastic resetting. The results demonstrate that time-modulated resetting protocols can be more advantageous in accelerating the completion of a target search process compared to constant-probability resetting.
Article
Physics, Fluids & Plasmas
Feng Huang, Hanshuang Chen
Summary: This study investigates discrete-time random walks with first-passage resetting processes on arbitrary networks, deriving exact expressions for stationary occupation probability, average number of resets, and mean first-passage time. Results show that these quantities can be expressed in terms of the fundamental matrix, demonstrating the advantage of first-passage resetting in global search on various networks.
Article
Mathematics
Massimiliano Turchetto, Michele Bellingeri, Roberto Alfieri, Ngoc-Kim-Khanh Nguyen, Quang Nguyen, Davide Cassi
Summary: Investigating the network response to node removal and the efficacy of the node removal strategies is fundamental to network science. In this study, we propose four new measures of node centrality based on random walk and compare them with existing strategies for synthesizing and real-world networks. The results indicate that the degree nodes attack is the best strategy overall, and the new node removal strategies based on random walk show the highest efficacy in relation to specific network topology.
Article
Physics, Fluids & Plasmas
Fei Ma, Ping Wang
Summary: The study proposes a simple algorithmic framework for generating power-law graphs with small diameters and examines their structural properties. The results show that these graphs have unique features such as density characteristics and higher trapping efficiency compared to existing scale-free models, confirmed through extensive simulations.
Article
Multidisciplinary Sciences
Alexandre Bovet, Jean-Charles Delvenne, Renaud Lambiotte
Summary: This article introduces a method based on a dynamical process evolving on a temporal network, which uncovers different dynamic scales in a system by considering the ordering of edges in forward and backward time. The method provides a new approach to extracting a simplified view of time-dependent network interactions in a system.
Article
Computer Science, Information Systems
Lucas Guerreiro, Filipi N. Silva, Diego R. Amancio
Summary: Discovery processes in network science focus on knowledge acquisition through exploring nodes. Different learning strategies can lead to the same learning performance, indicating the need to combine learning curves with other sequence features for inferring network topology.
INFORMATION SCIENCES
(2021)
Article
Physics, Multidisciplinary
Yan Wang, Xinxin Ca, Tongfeng Weng, Huijie Yang, Changgui Gu
Summary: In this study, we introduced lowest-degree preference random walks on complex networks, which significantly reduced search time compared to random walks on the majority of real networks. The optimal tuning parameter showed a strong positive correlation with entropy of degree sequence, indicating how much the search time could be reduced. This work opens up a new path for designing efficient search strategies with only local information available.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2021)
Article
Business
Alberto Arcagni, Rosanna Grassi, Silvana Stefani, Anna Torriero
Summary: Assortativity by degree in complex networks is quantified by the Newman coefficient, indicating a tendency for nodes to be connected to others with a similar degree. This study proposes a new class of higher-order assortativity measures for weighted networks, showing effectiveness in social networks. Applications to Facebook and co-authorship networks analyze assortativity beyond nearest neighbors.
JOURNAL OF BUSINESS RESEARCH
(2021)
Article
Mathematics, Interdisciplinary Applications
Andrei A. Klishin, Dani S. Bassett
Summary: Random walks are commonly used as a model for exploring and discovering complex networks. Exposure theory, a statistical mechanics framework, is introduced to predict the learning of nodes and edges in various types of networks and demonstrates a universal trajectory for edge learning.
JOURNAL OF COMPLEX NETWORKS
(2022)
Article
Multidisciplinary Sciences
Alexander Ponomarenko, Leonidas Pitsoulis, Marat Shamshetdinov
Summary: The LPAM method introduces a new approach for detecting overlapping communities in networks, considering different distance functions and evaluating its performance on real life instances and synthetic network benchmarks. It utilizes link partitioning and partitioning around medoids to detect overlapping communities in graphs.
Article
Physics, Multidisciplinary
Alejandro P. Riascos, Francisco Hernandez Padilla
Summary: In this paper, a framework for comparing differences in occupation probabilities of two random walk processes on networks is presented. The framework considers modifications of the network or the transition probabilities between nodes. A dissimilarity measure is defined using the eigenvalues and eigenvectors of the normalized Laplacian. The framework is used to examine differences in diffusive dynamics, the effect of new edges and rewiring in networks, and divergences in transport in degree-biased random walks and random walks with stochastic reset.
JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL
(2023)
Article
Multidisciplinary Sciences
Pauline Formaglio, Marina E. Wosniack, Raphael M. Tromer, Jaderson G. Polli, Yuri B. Matos, Hang Zhong, Ernesto P. Raposo, Marcos G. E. da Luz, Rogerio Amino
Summary: Plasmodium sporozoites actively migrate in the dermis and enter blood vessels to induce infection. Through intravital imaging, researchers found that sporozoites adopt a strategy of alternating global superdiffusive skin exploration and local subdiffusive blood vessel exploitation, enabling them to find intravasation hotspots associated with pericytes, enter the bloodstream and initiate malaria infection.
NATURE COMMUNICATIONS
(2023)
Article
Chemistry, Medicinal
Ran Liu, Xiang Liu, Jie Wu
Summary: In this study, we propose molecular descriptors based on persistent path-spectral and a machine learning model based on persistent path-spectral for the prediction of protein-ligand binding affinity. Our model combines the molecular descriptors from persistent path-spectral attributes with the gradient boosting tree machine learning model. We test this model on three commonly used datasets and achieve competitive results.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2023)
Article
Physics, Multidisciplinary
Yanik-Pascal Forster, Luca Gamberi, Evan Tzanis, Pierpaolo Vivo, Alessia Annibale
Summary: In this study, a novel method is proposed for calculating mean first-passage times (MFPTs) for random walks on graphs using dimensionality reduction technique. The method preserves the MFPTs between certain nodes and provides explicit formulae for MFPTs in specific graph structures. For other types of graphs, the generalized approximation method gives useful results.
JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL
(2022)
Article
Environmental Studies
Eric K. Tokuda, Henrique F. de Arruda, Guilherme S. Domingues, Luciano da F. Costa, Florence As Shibata, Roberto M. Cesar-Jr, Cesar H. Comin
Summary: The study investigates the interaction between cities and their green regions, and simulates the diffusion of green effects to better understand their influences on urban areas, particularly in terms of temperature, humidity, and gas exchanges. The approach involves automatically identifying the green regions, eliminating artifacts, and applying convolutions to analyze the diffusion dynamics. The study finds that even smaller green regions can significantly contribute to the diffusion of green effects in urban areas.
ENVIRONMENT AND PLANNING B-URBAN ANALYTICS AND CITY SCIENCE
(2023)
Article
Mathematics, Interdisciplinary Applications
Paulo C. Ventura, Eric K. Tokuda, Luciano da F. Costa, Francisco A. Rodrigues
Summary: Mathematical models, especially epidemic models, are fundamental for studying the course of diseases and planning control policies. This study proposes a numerical approximation approach using a Markov chain method to predict the long-term prevalence of a disease in metapopulations with an attraction landscape. The results are compared to a mathematic-analytical approach and show substantial agreement. The study also investigates the impact of different levels of attraction landscapes on disease propagation and the local scale of the population.
CHAOS SOLITONS & FRACTALS
(2023)
Article
Physics, Multidisciplinary
Luciano da F. Costa
Summary: The present work explores the application of multiset similarity, specifically the real-valued Jaccard and coincidence indices, in artificial neurons. Other similarity measures such as overlap/interiority index, cosine similarity, and Euclidean distance are also considered. The study focuses on the features used to characterize input patterns and addresses issues related to selectivity, sensitivity, and perturbations. The results show that the real-valued Jaccard and coincidence approaches outperform the interiority index and cross-correlation, with the coincidence-based neurons demonstrating enhanced overall performance. The potential of multiset neurons in image segmentation and their implications in various fields are also discussed.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2023)
Article
Physics, Multidisciplinary
Alexandre Benatti, Henrique Ferraz de Arruda, Filipi Nascimento Silva, Cesar Henrique Comin, Luciano da Fontoura Costa
Summary: Citation networks can provide valuable insights into the development of science and the connections between different knowledge areas. This study examines the robustness of citation network communities in relation to the keywords used to collect relevant articles. The findings show that the structure of citation network communities tends to remain intact even when certain keywords are absent, thanks to their highly modular nature. Additionally, a simple model is used to explore the relationship between keywords and the community structure, while also capturing the impact of missing keywords in different scenarios.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2023)
Article
Physics, Multidisciplinary
M. Alexandre, F. Xavier, T. Silva, F. Rodrigues
Summary: In this paper, the determinants of individual nestedness contribution (INC) in financial systems were assessed using data from two Brazilian financial networks. The study found that in the bank-firm credit network, the main determinants of INC were degree and core number, while in the interbank network, the INC of lending banks was mainly driven by their degree and there was no clear main determinant for borrowing banks' INC.
Article
Physics, Multidisciplinary
Alexandre Benatti, Luciano da F. Costa
Summary: This study addresses the important problem of reconstructing hierarchical structures in tree-like structures when sampling or discovering nodes. The research uses a simple tree model and coincidence similarity to quantify reconstruction errors, considering the effects of hierarchical structure, nodes content, and uncertainty. The study provides interesting results, including the dependence of accuracy on uncertainty parameter values and tree types, and the impact of content parameter changes on hierarchical reconstructions.
JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL
(2023)
Editorial Material
Biology
Francisco A. Rodrigues
PHYSICS OF LIFE REVIEWS
(2023)
Article
Physics, Multidisciplinary
Francisco A. Rodrigues
Summary: This review provides a brief overview of machine learning in physics, covering the main concepts of supervised, unsupervised, and reinforcement learning, as well as more specialized topics such as causal inference, symbolic regression, and deep learning. The principal applications of machine learning in physics are presented, and the associated challenges and perspectives are discussed.
Article
Computer Science, Information Systems
Barbara C. E. Souza, Filipi N. Silva, Henrique F. de Arruda, Giovana D. da Silva, Luciano Da F. Costa, Diego R. Amancio
Summary: This article introduces a method called "recurrence network" which can be used to analyze text narratives at multiple scales. By applying this method to the analysis of 300 books, the study found that recurrence networks can be effective in distinguishing meaningful and meaningless texts, as well as different literary genres.
INFORMATION SCIENCES
(2023)
Article
Engineering, Biomedical
Caroline L. Alves, Thaise G. L. de O. Toutain, Joel Augusto Moura Porto, Patricia Maria de Carvalho Aguiar, Eduardo Ponde de Sena, Francisco A. Rodrigues, Aruane M. Pineda, Christiane Thielemann
Summary: This study presents a rigorous approach using machine learning and deep learning techniques to automate the diagnosis of schizophrenia. By analyzing functional magnetic resonance imaging and electroencephalogram datasets, the researchers established a model that achieved excellent classification results. The findings demonstrate that the topology and dynamics of brain networks in individuals with schizophrenia differ from those without the disorder, and EEG measurements outperformed complex networks in capturing the brain alterations associated with schizophrenia.
JOURNAL OF NEURAL ENGINEERING
(2023)
Letter
Multidisciplinary Sciences
Andre Calixto Goncalves, Rodolfo Valentim, Francisco Aparecido Rodrigues, Ivan Filipe Fernandes
Article
Mathematics, Interdisciplinary Applications
Luciano da Fontoura Costa
Summary: The concepts of auto- and cross-correlation are important in various fields such as signal processing, pattern recognition, and physics. This study introduces the concept of multiset similarity, specifically the coincidence similarity index, for comparing similarity between networks. These operations enable the comparison of nodes and graphs in their respective neighborhoods. Furthermore, the potential of applying these methods to model-theoretic and real world networks is discussed, along with the analysis of individual autorrelation signatures in coincidence similarity networks.
JOURNAL OF PHYSICS-COMPLEXITY
(2022)
Article
Mathematics, Interdisciplinary Applications
Guilherme S. Dominguese, Eric K. Tokuda, Luciano da F. Costa
Summary: This study presents a methodology for automatically identifying motifs in street networks, obtained from city plans. The identified motifs are characterized and discussed from various perspectives, and the impact of the adopted features on the networks is analyzed. Additionally, a simple supervised learning method is introduced for assigning reference motifs to cities.
JOURNAL OF PHYSICS-COMPLEXITY
(2022)
Article
Mathematics, Interdisciplinary Applications
Luciano da Fontoura Costa
Summary: Complex networks are widely used in network science to represent and model various structures and phenomena. This study introduces two real-valued methods for translating generic datasets into networks and demonstrates their improved performance compared to other methods, as well as their ability to provide detailed descriptions and emphasize the modular structure of networks.
JOURNAL OF PHYSICS-COMPLEXITY
(2022)
Article
Biochemical Research Methods
Aline Silva da Cruz, Maria Margarida Drehmer, Wagner Baetas-da-Cruz, Joao Carlos Machado
Summary: This study quantified microcirculation cerebral blood flow in a rat model of ischemic stroke using ultrasound biomicroscopy and ultrasound contrast agents. The results showed high sensitivity and specificity of this method, making it a valuable tool for preclinical studies.
JOURNAL OF NEUROSCIENCE METHODS
(2024)
Article
Biochemical Research Methods
Christina Dalla, Ivana Jaric, Pavlina Pavlidi, Georgia E. Hodes, Nikolaos Kokras, Anton Bespalov, Martien J. Kas, Thomas Steckler, Mohamed Kabbaj, Hanno Wuerbel, Jordan Marrocco, Jessica Tollkuhn, Rebecca Shansky, Debra Bangasser, Jill B. Becker, Margaret McCarthy, Chantelle Ferland-Beckham
Summary: Many funding agencies have emphasized the importance of considering sex as a biological variable in experimental design to improve the reproducibility and translational relevance of preclinical research. Omitting the female sex from experimental designs in neuroscience and pharmacology can result in biased or limited understanding of disease mechanisms. This article provides methodological considerations for incorporating sex as a biological variable in in vitro and in vivo experiments, including the influence of age and hormone levels, and proposes strategies to enhance methodological rigor and translational relevance in preclinical research.
JOURNAL OF NEUROSCIENCE METHODS
(2024)
Article
Biochemical Research Methods
Wenyu Gu, Dongxu Li, Jia-Hong Gao
Summary: We developed a precise and rapid method for positioning and labelling triaxial OPMs on a wearable magnetoencephalography (MEG) system, improving the efficiency of OPM positioning and labelling.
JOURNAL OF NEUROSCIENCE METHODS
(2024)
Article
Biochemical Research Methods
Kai Lin, Linhang Zhang, Jing Cai, Jiaqi Sun, Wenjie Cui, Guangda Liu
Summary: The article introduces an EEG feature map processing model for emotion recognition, which achieves significantly improved accuracy by fusing EEG information at different spatial scales and introducing a channel attention mechanism.
JOURNAL OF NEUROSCIENCE METHODS
(2024)
Article
Biochemical Research Methods
John E. Parker, Asier Aristieta, Aryn H. Gittis, Jonathan E. Rubin
Summary: This work presents a toolbox that implements a methodology for automated classification of neural responses based on spike train recordings. The toolbox provides a user-friendly and efficient approach to detect various types of neuronal responses that may not be identified by traditional methods.
JOURNAL OF NEUROSCIENCE METHODS
(2024)
Article
Biochemical Research Methods
Yun Liang, Ke Bo, Sreenivasan Meyyappan, Mingzhou Ding
Summary: This study compared the performance of SVM and CNN on the same datasets and found that CNN achieved consistently higher classification accuracies. The classification accuracies of SVM and CNN were generally not correlated, and the heatmaps derived from them did not overlap significantly.
JOURNAL OF NEUROSCIENCE METHODS
(2024)
Article
Biochemical Research Methods
Antonino Visalli, Maria Montefinese, Giada Viviani, Livio Finos, Antonino Vallesi, Ettore Ambrosini
Summary: This study introduces an analytical strategy that allows the use of mixed-effects models (LMM) in mass univariate analyses of EEG data. The proposed method overcomes the computational costs and shows excellent performance properties, making it increasingly important in the field of neuroscience.
JOURNAL OF NEUROSCIENCE METHODS
(2024)
Article
Biochemical Research Methods
Xavier Cano-Ferrer, Alexandra Tran -Van -Minh, Ede Rancz
Summary: This study developed a novel rotation platform for studying neural processes and spatial navigation. The platform is modular, affordable, and easy to build, and can be driven by the experimenter or animal movement. The research demonstrated the utility of the platform, which combines the benefits of head fixation and intact vestibular activity.
JOURNAL OF NEUROSCIENCE METHODS
(2024)
