Article
Physics, Multidisciplinary
Kiriil Kovalenko, Irene Sendina-Nadal, Nagi Khalil, Alex Dainiak, Daniil Musatov, Andrei M. Raigorodskii, Karin Alfaro-Bittner, Baruch Barzel, Stefano Boccaletti
Summary: The paper introduces a mechanism for generating synthetic simplicial complexes that display desired statistical properties observed in the real world, and can be straightforwardly extended to higher-order structures. The model constructs networks with scale-free degree distribution and bounded or scale-free generalized degree distribution through preferential and/or nonpreferential attachment mechanisms, providing analytical control of scaling exponents to construct synthetic complexes with desired statistical properties.
COMMUNICATIONS PHYSICS
(2021)
Article
Physics, Fluids & Plasmas
Haoran Geng, Meng Cao, Chengwen Guo, Chenglei Peng, Sidan Du, Jie Yuan
Summary: A global disassortative rewiring strategy is proposed in this paper to enhance the robustness of scale-free networks against localized attacks. Validations show that this strategy outperforms others and offers a significant advantage in computational efficiency by avoiding localized-robustness measurement calculations during each rewire operation.
Article
Multidisciplinary Sciences
Giorgio Nicoletti, Leonardo Saravia, Fernando Momo, Amos Maritan, Samir Suweis
Summary: Between 2019 and 2020, Australia experienced a severe bushfire season due to climate change and anthropogenic transformations. Satellite imaging data from 2000 to 2020 showed that the peak in 2019-2020 was associated with critical points and percolation transition, indicating system-size outbreaks of fires. A forest-fire model was used to analyze the behavior of these emergent fire outbreaks, revealing the existence of an absorbing phase transition that could lead to irrecoverable vegetation loss.
Article
Mechanics
S. M. Oh, Y. Lee, J. Lee, B. Kahng
Summary: This paragraph discusses the study of homological percolation transitions (HPTs) and the results related to Betti numbers and coauthorship network evolution, highlighting significant changes in collaboration patterns. Analytic solutions for HPT with n = 0 and n = 1 are presented, exploring the characteristics of HPT in growing networks.
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT
(2021)
Article
Computer Science, Artificial Intelligence
Xiaoning Song, Yao Chen, Zhen-Hua Feng, Guosheng Hu, Dong-Jun Yu, Xiao-Jun Wu
Summary: The article presents a new Self-growing and Pruning Generative Adversarial Network (SP-GAN) for realistic image generation, which dynamically adjusts the network size and architecture, and utilizes an adaptive loss function. Experimental results show the method's advantages in stability and efficiency.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Farzaneh Kazemzadeh, Ali Asghar Safaei, Mitra Mirzarezaee, Sanaz Afsharian, Houman Kosarirad
Summary: With the increasing development of social networks, influence maximization has become an important research issue. In order to address the challenges of time efficiency and optimal selection of seed nodes, we propose the IMBC algorithm based on community structure. The algorithm improves time efficiency through optimal pruning and a minimum of dominating nodes, and selects seed nodes through scoring adjustment. Experimental results show that the algorithm outperforms other algorithms in influence spread and runtime.
Article
Computer Science, Artificial Intelligence
Jiangtao Li, Jianshe Wu, Weiquan He, Peng Zhou
Summary: The proposed Deep Aggregation Network (DAN) utilizes a layer-wise greedy optimization strategy by stacking multiple sequentially trained base models to tackle high order neighborhood aggregation, adopting a dynamic programming approach to eliminate the recursive nature. In addition, the introduction of reverse random walk along with classic random walk forms a novel sampling strategy, allowing DAN to flexibly adapt to different tasks. Extensive experiments on both synthetic and real-world networks demonstrate the effectiveness and efficiency of DAN, especially in handling large-scale networks with dense connections.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2021)
Article
Physics, Fluids & Plasmas
Nikita Frolov, Alexander Hramov
Summary: This paper explores the extension of self-organized bistability (SOB) on scale-free networks under coupling constraints. The study finds that SOB on scale-free networks originates from facilitated criticality and replicates extreme properties of epileptic seizure recurrences.
Article
Physics, Multidisciplinary
Marcus V. Alves Ribeiro, Aurel Jurjiu, Mircea Galiceanu
Summary: The relaxation dynamics of a new type of hyperbranched polymer networks constructed using a degree distribution specific to scale-free networks are studied. The topology of the networks is controlled by three parameters: gamma, K-min, and K-max. The influence of these parameters and the stiffness parameter q on the relaxation quantities is investigated. Various scaling behaviors are observed in the dynamical quantities for different combinations of the parameters.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2022)
Article
Mathematics, Applied
Mengyuan Chen, Yan Zhang, Zhang Zhang, Lun Du, Shuo Wang, Jiang Zhang
Summary: Network structures are crucial in various systems, but real cases often have incomplete or unavailable observable nodes and connections. This paper proposes a data-driven deep learning model called GIN, which infers the unknown parts of a network structure and the initial states of observable nodes using time series data from network dynamics. Experimental results demonstrate up to 90% accuracy in inferring the unknown parts and linear accuracy decline with the increase of unobservable nodes. This framework has wide applications when network structure is hard to obtain and time series data is rich.
Article
Computer Science, Hardware & Architecture
Himanshu Sharma, Elise Jennings
Summary: Bayesian neural networks offer statistical uncertainties for predictions, but with higher computational cost; this study examines the use of high-performance computing and distributed training to address challenges in training BNNs at scale, demonstrating the potential of network pruning to improve inference speed with minimal accuracy loss.
JOURNAL OF SUPERCOMPUTING
(2021)
Article
Computer Science, Information Systems
Jinlong Ma, Junfeng Zhang, Yongqiang Zhang
Summary: A new gravitation path routing strategy is proposed in this paper, which aims to improve network traffic capacity by selecting optimal paths based on the gravitational centrality of nodes. Simulation results show that this strategy is more effective than a previous efficient routing strategy proposed by Yan et al.
Article
Agriculture, Multidisciplinary
Luciana Alves Caldeira Brant, Claudia Rita de Souza, Renata Vieira da Mota, Fernanda de Paula Fernandes, Mariana Gabriele Marcolino Goncalves, Michele Duarte de Menezes, Isabela Peregrino, Nilton Curi, Murillo de Albuquerque Regina
Summary: The shift in grape harvest timing from wet summer to dry winter in Southeast Brazil through double-pruning management has led to an improvement in the quality of winter wines. Differences in vineyard vigor were more related to management practices, while grape quality in terms of sugar and acidity was influenced by soil sand content and winter temperature. Overall, the high thermal range and low precipitation during autumn-winter were found to be the main factors contributing to the improvement of phenolic compounds in grapes.
Article
Computer Science, Artificial Intelligence
Michaela Urbanovska, Antonin Komenda
Summary: Automated planning for problems without explicit models is a challenging research area. The combination of deep learning and classical planning can provide a solution for variably scaled problems without the need for a human-encoded model. Experimental evaluation is provided to compare the implemented techniques with classical model-based methods.
KNOWLEDGE AND INFORMATION SYSTEMS
(2021)
Article
Multidisciplinary Sciences
Linqing Liu, Mengyun Shen, Chang Tan
Summary: The study found that approximately 38% to 69% of product networks in capacity-weighted networks exhibit scale-free characteristics, with differences in scale-free characteristics depending on the technology. Contrary to expectations, exports of high-technology products are not concentrated in a few developed countries.
SCIENTIFIC REPORTS
(2021)
Article
Multidisciplinary Sciences
Xuelong Li, Marko Jusup, Zhen Wang, Huijia Li, Lei Shi, Boris Podobnik, H. Eugene Stanley, Shlomo Havlin, Stefano Boccaletti
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2018)
Article
Mathematics, Interdisciplinary Applications
Muhua Zheng, Wei Wang, Ming Tang, Jie Zhou, S. Boccaletti, Zonghua Liu
CHAOS SOLITONS & FRACTALS
(2018)
Article
Physics, Multidisciplinary
Elena Pitsik, Vladimir Makarov, Daniil Kirsanov, Nikita Frolov, Mikhail Goremyko, Xuelong Li, Zhen Wang, Alexander Hramov, Stefano Boccaletti
NEW JOURNAL OF PHYSICS
(2018)
Article
Physics, Multidisciplinary
Johann H. Martinez, Stefano Boccaletti, Vladimir V. Makarov, Javier M. Buldu
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2018)
Article
Multidisciplinary Sciences
I. Leyva, I. Sendina-Nadal, R. Sevilla-Escoboza, V. P. Vera-Avila, P. Chholak, S. Boccaletti
SCIENTIFIC REPORTS
(2018)
Article
Multidisciplinary Sciences
Zhen Wang, Marko Jusup, Lei Shi, Joung-Hun Lee, Yoh Iwasa, Stefano Boccaletti
NATURE COMMUNICATIONS
(2018)
Article
Multidisciplinary Sciences
Tian Qiu, Ivan Bonamassa, Stefano Boccaletti, Zonghua Liu, Shuguang Guan
SCIENTIFIC REPORTS
(2018)
Article
Physics, Multidisciplinary
Michael M. Danziger, Ivan Bonamassa, Stefano Boccaletti, Shlomo Havlin
Article
Mathematics, Applied
Chen Chu, Xintao Hu, Chen Shen, Tong Li, Stefano Boccaletti, Lei Shi, Zhen Wang
Article
Engineering, Mechanical
Lei Shi, Chen Shen, Yini Geng, Chen Chu, Haoran Meng, Matjaz Perc, Stefano Boccaletti, Zhen Wang
NONLINEAR DYNAMICS
(2019)
Article
Physics, Multidisciplinary
Xue Li, Tian Qiu, Stefano Boccaletti, Irene Sendina-Nadal, Zonghua Liu, Shuguang Guan
NEW JOURNAL OF PHYSICS
(2019)
Article
Physics, Fluids & Plasmas
Jian-Fang Zhou, Wu-Jie Yuan, Debao Chen, Bing-Hong Wang, Zhao Zhou, Stefano Boccaletti, Zhen Wang
Article
Mathematics, Applied
Inmaculada Leyva, Irene Sendina-Nadal, Stefano Boccaletti
DISCRETE AND CONTINUOUS DYNAMICAL SYSTEMS-SERIES B
(2018)
Article
Physics, Fluids & Plasmas
Vanesa Avalos-Gaytan, Juan A. Almendral, I. Leyva, F. Battiston, V. Nicosia, V. Latora, S. Boccaletti
Article
Physics, Fluids & Plasmas
Wu-Jie Yuan, Jian-Fang Zhou, Irene Sendina-Nadal, Stefano Boccaletti, Zhen Wang