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
Public, Environmental & Occupational Health
Zhiagng Tao, Haibo Zhang
Summary: This study aims to identify the driving forces behind interorganizational networks in China following disasters. Using the theory of complex adaptive systems, the self-organization process of disaster response is identified as the network formation process. Interorganizational networks that emerged in response to natural hazards and technical disasters are analyzed using an exponential random model. The findings suggest that bonding structures and sector/jurisdiction-based homophily effects play crucial roles in network formation for disaster response. Five propositions describing the network formation process in China's disaster response are proposed based on the research findings. This theoretical model is essential for advancing research and practice in complex disaster network management.
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
Computer Science, Information Systems
Amr Elsisy, Aamir Mandviwalla, Boleslaw K. Szymanski, Thomas Sharkey
Summary: The study focuses on organizational structures in covert networks, introducing a novel method to rewire these networks parameterized by edge connectivity standard deviation. It models higher-level organizational structures as a multi-layer network and lowest level using the Stochastic Block Model. Synthetic networks provide alternative structures for original network data, enabling analysts to find stable structures and conduct perturbation tests.
INFORMATION SCIENCES
(2022)
Article
Physics, Multidisciplinary
Yang Li, Hao Sun, Wanda Xiong, Genjiu Xu
Summary: A belief model on random networks was proposed to study the process of opinion dissemination, focusing on individual inherent beliefs, persuasion ability heterogeneity, and the dilution effect of neighbor size. Theoretical final fraction of active agents was determined through mean-field approximation, showing divergence around critical conditions due to heterogeneity of initial active node properties. Two strategies for selecting the initial active node based on its properties were proposed as alternatives for predicting and controlling contagion, with their efficiencies discussed through simulations on different networks.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2021)
Article
Mechanics
Zu-Yu Qian, Cheng Yuan, Jie Zhou, Shi-Ming Chen, Sen Nie
Summary: This study explores the incorporation of conformity behavior into network control and finds that controlling undirected networked systems with conformity becomes easier after the network connectivity reaches a critical point. The research also identifies key nodal structural characteristics and proposes an optimal control strategy to reduce energy consumption. These findings are validated in synthetic and real networks, highlighting their significance in describing control energy in networked systems.
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT
(2022)
Article
Computer Science, Information Systems
Jie Zhao, Tao Wen, Hadi Jahanshahi, Kang Hao Cheong
Summary: The article introduces the methods for identifying influential nodes in complex networks and the existing problems. It proposes a random walk-based gravity model to solve this problem, and through experiments, it demonstrates the superior performance of the model.
INFORMATION SCIENCES
(2022)
Article
Physics, Multidisciplinary
Jiating Yu, Jiacheng Leng, Duanchen Sun, Ling-Yun Wu
Summary: Network models are widely used in various fields for their ability to represent relationships between variables. Network structure can be unclear due to factors like experimental noise and missing data, hindering downstream analyses such as community detection. Therefore, network denoising is necessary before analysis. However, the importance of network pre-processing for community detection has been neglected. In this study, a novel network denoising method, called Network Refinement (NR), was proposed to enhance the self-organization properties of complex networks through a global diffusion process. NR significantly improved the clarity of the network's mesoscale structure and boosted the performance of various community detection algorithms.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Kamal Berahmand, Elahe Nasiri, Saman Forouzandeh, Yuefeng Li
Summary: This article proposes an improved method for local random walk by encouraging the movement towards nodes with stronger influence, resulting in higher prediction accuracy. A comparison with other similarity-based methods was conducted on 11 real-world networks, and the results demonstrated its superior performance in link prediction.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Biochemical Research Methods
Wei Wang, Wei Liu
Summary: The study suggests that prognostic models based on protein complexes outperform those based on individual genes in predicting patient prognosis and identifying risk protein complexes, providing a more comprehensive understanding of molecular mechanisms related to cancer progression.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Physics, Multidisciplinary
Noam Abadi, Franco Ruzzenenti
Summary: Complex networks is a discipline aimed at understanding large interacting systems. This research establishes a relation between the interactions of a system and the networks structure that emerges. Using a Lennard-Jones particle system as an example, the study demonstrates how the physical arrangement of interacting particles can be interpreted as a binary approximation to the interaction potential. This approximation simplifies the calculation of the partition function of the system and allows for the study of the stability of the interaction structure. The results from simulations and the approximated partition function are compared to show the complementarity of the network and system perspective.
Article
Mathematics, Interdisciplinary Applications
Yan Wang, Xinxin Cao, Tongfeng Weng, Huijie Yang, Changgui Gu
Summary: The study introduces a mixed strategy called multi-biased random walk on complex networks, which quantifies the time required to find a target through an analytical expression and reveals that its global mean first passage time follows a specific pattern. These findings are confirmed through numerical and theoretical results on various networks.
CHAOS SOLITONS & FRACTALS
(2021)
Article
Biochemistry & Molecular Biology
Ying Wang, Lin-Lin Wang, Leon Wong, Yang Li, Lei Wang, Zhu-Hong You
Summary: Protein is the fundamental organic substance in cells that plays a crucial role in biological activities. Self-interacting protein (SIP) is an important protein interaction. This study presents a SIP prediction method, SIPGCN, using a deep learning graph convolutional network (GCN). The results demonstrate excellent performance of SIPGCN.
Review
Physics, Multidisciplinary
Vito Dichio, Fabrizio De Vico Fallani
Summary: The brain is a highly complex system with intermingled connections that give rise to rich dynamics and high-level cognitive functions. Analyzing brain networks is challenging because their structure represents only one possible realization of a generative stochastic process. Maximum entropy models, such as exponential random graph models, provide an approach to identify the local connection mechanisms behind observed global network structure.
REPORTS ON PROGRESS IN PHYSICS
(2023)
Article
Computer Science, Interdisciplinary Applications
Esther S. Daus, Markus Fellner, Ansgar Juengel
Summary: A random-batch method for interacting particle systems is proposed, which can be used for multicomponent systems. This method reduces the computational cost by randomly dividing particles into batches while maintaining a certain accuracy. The numerical efficiency of this method is confirmed through testing and simulations.
JOURNAL OF COMPUTATIONAL PHYSICS
(2022)
Article
Multidisciplinary Sciences
Niklas Boers, Bedartha Goswami, Aljoscha Rheinwalt, Bodo Bookhagen, Brian Hoskins, Juergen Kurths
Article
Geography, Physical
Sebastian F. M. Breitenbach, Birgit Plessen, Sarah Waltgenbach, Rik Tjallingii, Jens Leonhardt, Klaus Peter Jochum, Hanno Meyer, Bedartha Goswami, Norbert Marwan, Denis Scholz
GLOBAL AND PLANETARY CHANGE
(2019)
Article
Geosciences, Multidisciplinary
Fiona J. Clubb, Bodo Bookhagen, Aljoscha Rheinwalt
JOURNAL OF GEOPHYSICAL RESEARCH-EARTH SURFACE
(2019)
Article
Geosciences, Multidisciplinary
Aljoscha Rheinwahlt, Bedartha Goswami, Bodo Bookhagen
JOURNAL OF GEOPHYSICAL RESEARCH-EARTH SURFACE
(2019)
Article
Multidisciplinary Sciences
M. Singh, R. Krishnan, B. Goswami, A. D. Choudhury, P. Swapna, R. Vellore, A. G. Prajeesh, N. Sandeep, C. Venkataraman, R. Donner, N. Marwan, J. Kurths
Article
Multidisciplinary Sciences
Dominik Traxl, Niklas Boers, Aljoscha Rheinwalt, Bodo Bookhagen
Summary: The study investigates the relationship between temperature and extreme rainfall using global data, showing a negative correlation in tropical oceans due to cyclonic activity. This highlights the importance of considering circulation dynamics in understanding the influence of global warming on changing rainfall extremes.
NATURE COMMUNICATIONS
(2021)
Article
Physics, Multidisciplinary
Harshit Agrawal, Ashwin Lahorkar, Snehal M. Shekatkar
Summary: The exchange of resources in systems such as social networks and networks of cities and villages plays a crucial role in understanding the dynamics of resource distribution in complex networks. Introducing the concept of money in the network significantly improves the survivability of scale-free networks.
Article
Geosciences, Multidisciplinary
Felix M. Strnad, Jakob Schloer, Christian Froehlich, Bedartha Goswami
Summary: This study uses surface air temperature data to construct climate networks of El Nino events and applies Forman-Ricci curvature to distinguish regional links from teleconnections. The results show that both EP and CP types of El Nino influence teleconnection patterns, but with different spatial manifestations. EP El Ninos alter the general circulation and result in primarily tropical teleconnections, while CP El Ninos show only subtle changes to normal conditions.
GEOPHYSICAL RESEARCH LETTERS
(2022)
Article
Biochemical Research Methods
Dhiraj Kumar Hazra, Bhalchandra S. Pujari, Snehal M. Shekatkar, Farhina Mozaffer, Sitabhra Sinha, Vishwesha Guttal, Pinaki Chaudhuri, Gautam Menon
Summary: Estimating the burden of COVID-19 in India is difficult due to undercounting of cases and deaths. This study analyzed the first wave of the pandemic in India using the INDSCI-SIM model and Bayesian methods. The findings suggest that deaths were undercounted by a factor of 2-5 and cases were undercounted by a factor of 20-25 towards the end of the first wave. The infection fatality ratio was estimated to be in the range of 0.05-0.15, and approximately 35% of India's population had been infected by the end of the first wave.
PLOS COMPUTATIONAL BIOLOGY
(2022)
Article
Multidisciplinary Sciences
Felix M. Strnad, Jakob Schloer, Ruth Geen, Niklas Boers, Bedartha Goswami
Summary: This study characterizes the propagation modes of rainfall extremes in the Indo-Pacific region driven by the Boreal Summer Intraseasonal Oscillation. Pacific sea surface temperatures are found to modulate the propagation of the oscillation, influencing the occurrence of extreme rainfall. The study also demonstrates the potential for early warning of rainfall extremes in the region up to four weeks in advance.
NATURE COMMUNICATIONS
(2023)
Article
Geosciences, Multidisciplinary
Abhirup Banerjee, Bedartha Goswami, Yoshito Hirata, Deniz Eroglu, Bruno Merz, Juergen Kurths, Norbert Marwan
Summary: Identifying recurrences in extreme event-like time series is challenging due to rare occurrence and large temporal gaps. Existing time series analysis techniques are not directly applicable, but a modified edit distance method can be used to quantify deterministic properties and serial dependency in flood time series.
NONLINEAR PROCESSES IN GEOPHYSICS
(2021)
Review
Engineering, Mechanical
Bedartha Goswami
Article
Geography, Physical
Taylor Smith, Aljoscha Rheinwalt, Bodo Bookhagen
EARTH SURFACE DYNAMICS
(2019)