Article
Multidisciplinary Sciences
Ivan V. Kozitsin
Summary: We introduce a flexible opinion formation model that can simulate various micro-influence assumptions and models. The model can be calibrated using real data and bridges theoretical studies and empirical research. Through analysis and simulations, we demonstrate the model's effectiveness and successfully generate an artificial society similar to real ones.
SCIENTIFIC REPORTS
(2022)
Article
Neurosciences
David A. Wood, Sina Kafiabadi, Ayisha Al Busaidi, Emily Guilhem, Antanas Montvila, Jeremy Lynch, Matthew Townend, Siddharth Agarwal, Asif Mazumder, Gareth J. Barker, Sebastien Ourselin, James H. Cole, Thomas C. Booth
Summary: Convolutional neural networks can accurately predict age in healthy individuals, with implications for clinical decision-making and optimizing patient pathways. A brain-age framework suitable for routine clinical head MRI examinations was developed, enabling real-time detection of older-appearing brains.
Article
Physics, Fluids & Plasmas
Katsumi Chiyomaru, Kazuhiro Takemoto
Summary: This paper investigates adversarial attacks conducted to distort voter model dynamics in complex networks. A simple adversarial attack method is proposed to hold the state of opinions of an individual closer to the target state in the voter model dynamics. Results show that even when one opinion is the majority, the vote outcome can be inverted by strategically adding extremely small perturbations in social networks. Adversarial attacks are more effective in complex networks, indicating that opinion dynamics can be unknowingly distorted.
Article
Biochemical Research Methods
Oliver Schmitt, Christian Nitzsche, Peter Eipert, Vishnu Prathapan, Marc-Thorsten Huett, Claus C. Hilgetag
Summary: Connectomes provide comprehensive descriptions of neural connections, which are crucial for understanding central brain function and peripheral processing of neural signals. This study examines detailed connectomes with edge weighting and orientation properties, and investigates diffusion-reaction models to study dynamic concentration patterns in control and lesioned connectomes. The implementation of reaction-diffusion systems in the neuroVIISAS framework allows for the study of empirical connectomes and specific network models.
PLOS COMPUTATIONAL BIOLOGY
(2022)
Article
Geography
A. Stewart Fotheringham, Ziqi Li, Levi John Wolf
Summary: This article quantifies the impact of spatial context on voter preferences using a multiscale geographically weighted regression (MGWR) model, demonstrating the spatially varying impacts of determinants on voter preferences in the 2016 U.S. presidential election.
ANNALS OF THE AMERICAN ASSOCIATION OF GEOGRAPHERS
(2021)
Article
Physics, Fluids & Plasmas
Lucia Ramirez, Maxi San Miguel, Tobias Galla
Summary: In this study, we investigate the time evolution of the density of active links and the entropy of the distribution of agents among opinions in multistate voter models on uncorrelated networks with all-to-all interaction. We find that the density of active links decays exponentially, while the average entropy decays exponentially only when there are at most two opinions left in the population.
Article
Physics, Multidisciplinary
Robert Jankowski, Anna Chmiel
Summary: This paper studies the epidemic spreading model on multiplex networks, taking into account complex human behaviors, and investigates the interplay between epidemic spreading and opinion dynamics. The impact of different timescales of opinion dynamics on epidemic spreading is highlighted, focusing on the time and the infection's peak.
Article
Computer Science, Artificial Intelligence
Denis Paperno
Summary: Can recurrent neural networks, inspired by human sequential data processing, learn to understand language? The study constructs simplified data sets reflecting core properties of natural language, such as recursive syntactic structure and compositionality. The findings suggest that LSTM and GRU networks can generalize well in compositional interpretation, but only under certain learning conditions, including a well-paced curriculum, extensive training data, and left-to-right composition.
COMPUTATIONAL LINGUISTICS
(2022)
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
Computer Science, Artificial Intelligence
Helder Alves, Paula Brito, Pedro Campos
Summary: In this paper, the concept of interval-weighted networks (IWN) is introduced and developed as a novel approach in Social Network Analysis. The methodology is applied to two real-world networks and the findings suggest the effectiveness of this approach in understanding the structure of networks.
DATA MINING AND KNOWLEDGE DISCOVERY
(2023)
Article
Multidisciplinary Sciences
Yuanxiang Jiang, Meng Li, Ying Fan, Zengru Di
Summary: Measuring the dissimilarities between networks is a fundamental problem widely used in various fields. This study proposes a quantitative dissimilarity metric for weighted networks (WD-metric) based on the D-measure method, which can effectively capture the influence of weight on network structure. Experimental results demonstrate the effectiveness of WD-metric and its potential as a criterion for backbone extraction algorithms in complex networks.
SCIENTIFIC REPORTS
(2021)
Article
Computer Science, Artificial Intelligence
Longjie Li, Yanhong Wen, Shenshen Bai, Panfeng Liu
Summary: This paper proposes a new method for link prediction that adaptively assesses the connection likelihood of node pairs. Experimental results show that the proposed method outperforms other methods in terms of accuracy.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Information Systems
K. U. Jaseena, Binsu C. Kovoor
Summary: Weather forecasting is the practice of predicting the state of the atmosphere based on different weather parameters. Accurate weather forecasts are crucial in various fields. With the advancement of atmospheric observing systems and the increasing volume of weather data, deep learning techniques are being used to improve weather prediction. This paper provides a comprehensive review of weather forecasting approaches and discusses potential future research directions.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Physics, Multidisciplinary
Xuan He, Luyang Wang, Hongbo Zhu, Zheng Liu
Summary: In this study, complex weighted networks for seismicity based on spacetime influence domain were constructed for three different regions. It was found that high-weight links constitute a certain proportion in the networks. The analysis revealed that degree distribution, weight distribution, and nodal strength distribution of the weighted networks follow a power-law distribution, and both unweighted and weighted networks exhibit assortative mixing and hierarchical structures.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2021)
Article
Computer Science, Software Engineering
Javier De La Hoz-M, Susana Mendes, Maria Jose Fernandez-Gomez
Summary: GeoWeightedModel is an R package that provides a user-friendly web application for performing techniques from the Geographically Weighted (GW) models subarea of spatial statistics. It allows users to perform Geographically Weighted Regression, Geographically Weighted Principal Component Analysis, Geographically Weighted Discriminant Analysis, and calculate geographically weighted summaries. The tool aims to simplify the workflow and make it more accessible, especially for users unfamiliar with the R environment. It also provides mapping capabilities for visualizing the results and exploring spatial heterogeneity of the data.
Article
Mathematics, Applied
Giulio Iannelli, Giordano De Marzo, Claudio Castellano
Summary: Social media influences online activity by recommending content based on users' past preferences, leading to the formation of filter bubbles and limiting exposure to new or alternative content. Research shows that personalized recommendation algorithms contribute to the polarization of opinions.
Article
Multidisciplinary Sciences
Daniele Notarmuzi, Claudio Castellano, Alessandro Flammini, Dario Mazzilli, Filippo Radicchi
Summary: This study analyzes time-stamped events from several online social media platforms over a period of more than ten years and reveals the universality and criticality of information propagation in social media. The propagation of information can occur through simple or complex contagion processes, with the complexity of the process being correlated with the semantic content of the information being propagated.
NATURE COMMUNICATIONS
(2022)
Article
Multidisciplinary Sciences
Juan Ozaita, Andrea Baronchelli, Angel Sanchez
Summary: Visible markers have minimal impact on promoting cooperation, calling for further research.
SCIENTIFIC REPORTS
(2022)
Correction
Multidisciplinary Sciences
Amin Mekacher, Alberto Bracci, Matthieu Nadini, Mauro Martino, Laura Alessandretti, Luca Maria Aiello, Andrea Baronchelli
SCIENTIFIC REPORTS
(2022)
Article
Health Care Sciences & Services
Giulia de Meijere, Eugenio Valdano, Claudio Castellano, Marion Debin, Charly Kengne-Kuetche, Harold Noel, Joshua S. Weitz, Daniela Paolotti, Lisa Hermans, Niel Hens, Vittoria Colizza
Summary: European countries are focusing on testing, isolation, and boosting strategies to counter the winter surge of SARS-CoV-2 Omicron subvariants. However, pandemic fatigue and limited compliance may undermine mitigation efforts. A multicountry survey found that the majority of participants were willing to adhere to testing and isolation protocols, although there were differences in adherence to booster vaccination. The study's epidemic model results suggest that testing and isolation protocols can significantly reduce transmission, but cost barriers may decrease adherence and effectiveness.
LANCET REGIONAL HEALTH-EUROPE
(2023)
Article
Mathematics, Interdisciplinary Applications
Gabriele Di Bona, Alberto Bracci, Nicola Perra, Vito Latora, Andrea Baronchelli
Summary: Decentralization is a widely-used concept across disciplines such as Economics, Political Science, and Computer Science. Research shows that the number of papers on decentralization has been exponentially increasing since the 1950s. The study also reveals that Blockchain has become the most influential field, while Governance dominated in the earlier decades.
Article
Physics, Fluids & Plasmas
Daniele Notarmuzi, Alessandro Flammini, Claudio Castellano, Filippo Radicchi
Summary: We investigate the temporal statistics of avalanche dynamics in the SIS model on finite random networks at criticality. Our numerical simulations on annealed topologies reveal three distinct dynamical regimes in the survival probability. We find that the crossover timescales separating these regimes scale differently for homogeneous and heterogeneous networks. The qualitative understanding of the phenomenology is based on known features of SIS dynamics, while a fully quantitative approach using Langevin theory reproduces the results only for homogeneous networks.
Article
Physics, Fluids & Plasmas
Lorenzo Cirigliano, Claudio Castellano, Gabor Timar
Summary: The paper introduces the application of classical percolation theory in information transfer and discusses the requirement for communication between nodes in specific situations. The study finds that the interplay of extended range and heterogeneity leads to novel critical behavior in scale-free networks.
Article
Computer Science, Information Systems
Arnaldo Santoro, Alessandro Galeazzi, Teresa Scantamburlo, Andrea Baronchelli, Walter Quattrociocchi, Fabiana Zollo
Summary: The issue of vaccine hesitancy poses a challenge to controlling the Covid-19 pandemic, and this study focuses on the Twitter discourse surrounding Covid-19 vaccines. By analyzing temporal and geographical dimensions, the study finds differences in the interaction structure and reliability of sources across five countries. Major external events can lead to changes in online debate, but the suspension of the AstraZeneca vaccine did not significantly affect the production and consumption of misinformation related to Covid-19 vaccines.
SOCIAL NETWORK ANALYSIS AND MINING
(2023)
Article
Multidisciplinary Sciences
Romualdo Pastor-Satorras, Claudio Castellano
Summary: This article investigates the effects of interventions that provide asymmetric protection on the population level. The study finds that interventions that protect the adopter more efficiently are more effective in reducing the spread of the disease and the number of infected individuals.
SCIENTIFIC REPORTS
(2022)
Article
Physics, Fluids & Plasmas
Jaume Ojer, Romualdo Pastor-Satorras
Summary: This study investigates the impact of animal social networks with a weighted pattern of interactions on the flocking transition in models of self-organized collective motion. The results show that the presence of weights decreases the value of the flocking threshold and increases the fragility of the flocking state. The heterogeneity of the weight pattern influences the shift in the threshold.
Article
Multidisciplinary Sciences
Alberto Bracci, Joern Boehnke, Abeer ElBahrawy, Nicola Perra, Alexander Teytelboym, Andrea Baronchelli
Summary: This study analyzes large-scale datasets and finds that online marketplaces exhibit strikingly similar transaction patterns despite differences in language, lifetimes, products, regulation, and technology. Buyer behavior is influenced by the memory of past interactions, and a network formation model is proposed to explain these observations.
Article
Physics, Fluids & Plasmas
Lorenzo Cirigliano, Giulio Cimini, Romualdo Pastor-Satorras, Claudio Castellano
Summary: Percolation on networks is a common framework for modeling various processes, and cumulative merging percolation (CMP) is a long-range percolation process where the clusters formed do not coincide with the topologically connected components of the network. This study develops a more general formulation of CMP, showing a richer phase transition scenario.
Article
Multidisciplinary Sciences
Marco Mancastroppa, Andrea Guizzo, Claudio Castellano, Alessandro Vezzani, Raffaella Burioni
Summary: Effective contact tracing is crucial in controlling epidemic spreading. A study found that besides forward and backward tracing, there is also an indirect tracing when tracing large gatherings. This indirect tracing can detect infected asymptomatic individuals, even if they did not directly contact the index case. The study analyzes the contribution of different tracing mechanisms and suggests an optimal choice for tracing the sizes of gatherings.
JOURNAL OF THE ROYAL SOCIETY INTERFACE
(2022)
