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
Social Sciences, Interdisciplinary
Xueqin Huang, Xianqiang Zhu, Xiang Xu, Qianzhen Zhang, Ailin Liang
Summary: This paper proposes a general framework and two novel methods for handling tasks in large graphs. By partitioning the graphs into multiple subgraphs and using a parallel model to learn the dynamics of the subgraphs, dynamics can be learned efficiently. Experiments demonstrate the universality and superiority of the framework.
Article
Chemistry, Physical
Neda Zarayeneh, Nitesh Kumar, Ananth Kalyanaraman, Aurora E. Clark
Summary: This article introduces an algorithm called Delta-screening for identifying temporal communities. The algorithm is flexible in handling the evolving compositions, merging, and splitting behaviors within chemical networks, and is able to resolve multiple time scales.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
(2022)
Article
Mathematics, Applied
Gheorghe Craciun, Jiaxin Jin, Casian Pantea, Adrian Tudorascu
Summary: This paper examines the rate of convergence to complex balanced equilibrium in chemical reaction-diffusion systems with boundary equilibria. The study focuses on a three-species system and a two-species reversible reaction-diffusion network, proving results on convergence to positive equilibrium.
DISCRETE AND CONTINUOUS DYNAMICAL SYSTEMS-SERIES B
(2021)
Article
Biology
Radek Erban, Hye-Won Kang
Summary: This paper investigates chemical reaction networks with multiple stable limit cycles and presents bounds on the minimal number of chemical species and reactions required.
BULLETIN OF MATHEMATICAL BIOLOGY
(2023)
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)
Review
Automation & Control Systems
Xin Liu, Mingyu Yan, Lei Deng, Guoqi Li, Xiaochun Ye, Dongrui Fan
Summary: This paper categorizes and provides a comprehensive survey of sampling methods for efficient training of GCN. It compares the characteristics and differences of these methods in detail within each category, and further gives an overall analysis across all categories. Additionally, it discusses the challenges and future research directions of sampling methods.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2022)
Article
Computer Science, Information Systems
Joao B. Florindo, Young-Sup Lee, Kyungkoo Jun, Gwanggil Jeon, Marcelo K. Albertini
Summary: Visibility graphs are proposed for modeling the feature map of a neural network, showcasing competitive performance in texture image classification. The experiments confirm the potential of these techniques for providing more meaningful interpretation to the use of neural networks in different contexts.
INFORMATION SCIENCES
(2021)
Article
Engineering, Industrial
Jie Liu, Shuwen Zheng, Chong Wang
Summary: This paper proposes a causal graph attention network with disentangled representations (Causal-GAT) for fault detection. High-dimensional variables are transformed into directed acyclic graphs using data-driven causal discovery, and the causal graph is fed into Causal-GAT. The introduced Disentangled Causal Attention (DC-Attention) adaptively aggregates cause variables to embed effect variables. Experimental results demonstrate the advantages of the proposed method, and the validity of causal graph construction, representation disentanglement, and interpretability of the model are discussed.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Computer Science, Artificial Intelligence
Ruixin Ma, Fangqing Guo, Zeyang Li, Liang Zhao
Summary: This study proposes a Knowledge Graph Random Neural Networks (KRNN) for recommender systems, which addresses the issues of over-smoothing and data sparsity in existing graph neural network methods on knowledge graph. By utilizing a random dropout strategy and feature propagation method, the proposed KRNN achieves superior performance in predicting user preferences, especially in data sparse scenarios.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Darnbi Sakong, Thanh Trung Huynh, Thanh Tam Nguyen, Thanh Toan Nguyen, Jun Jo, Quoc Viet Hung Nguyen
Summary: This paper proposes a novel KG alignment framework, ComplexGCN, which learns the embeddings of both entities and relations in complex spaces while capturing both semantic and neighborhood information simultaneously. The proposed model ensures richer expressiveness and more accurate embeddings by successfully capturing various relation structures in complex spaces with high-level computation. The model further incorporates relation label and direction information with a low degree of freedom. Empirical results show the efficiency and effectiveness of the proposed method.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2023)
Review
Engineering, Civil
Dannier Xiao, Mehrdad Dianati, William Goncalves Geiger, Roger Woodman
Summary: Automated and autonomous vehicles require a reliable autonomous hazardous event detection system to operate without human supervision in complex road environments. The use of graph-based methods is a promising solution, as it allows for relational reasoning and organizing knowledge about the operational environment. This paper provides a comprehensive review of state-of-the-art graph-based methods, categorizing them as rule-based, probabilistic, and machine learning-driven, and also discusses available datasets and evaluation metrics for hazardous event detection.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Computer Science, Cybernetics
Bishenghui Tao, Hong-Ning Dai, Haoran Xie, Fu Lee Wang
Summary: This article focuses on modeling and understanding the blockchain transaction network of the metaverse systems from a structural identity perspective. The complex network analysis of three metaverse-related systems is conducted, and a novel representation learning method called SVRP is proposed. Node classification and link prediction tasks are performed using graph neural networks (GNNs). Empirical results demonstrate that the proposed SVRP outperforms other existing methods in multiple tasks.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2023)
Article
Chemistry, Multidisciplinary
Editha C. Jose, Dylan Antonio S. J. Talabis, Eduardo R. Mendoza
Summary: This paper presents initial results on the extension of the Horn and Jackson ACB Theorem, discussing the absolute complex balancing property of different types of kinetic systems, and introducing new methods for finding sufficient conditions for ACB.
MATCH-COMMUNICATIONS IN MATHEMATICAL AND IN COMPUTER CHEMISTRY
(2022)
Article
Chemistry, Multidisciplinary
Dzvenymyra Yarish, Sofiya Garkot, Oleksandr O. Grygorenko, Dmytro S. Radchenko, Yurii S. Moroz, Oleksandr Gurbych
Summary: This study presents a novel graph neural network architecture for predicting chemical yield. The network incorporates structural information, molecular descriptors, and reaction-level descriptors, and is able to handle incomplete chemical reactions and generate reactants-product atom mapping.
JOURNAL OF COMPUTATIONAL CHEMISTRY
(2023)
Review
Physics, Multidisciplinary
Marko Jusup, Petter Holme, Kiyoshi Kanazawa, Misako Takayasu, Ivan Romic, Zhen Wang, Suncana Gecek, Tomislav Lipic, Boris Podobnik, Lin Wang, Wei Luo, Tin Klanjscek, Jingfang Fan, Stefano Boccaletti, Matjaz Perc
Summary: In recent years, physics methods have been widely used to study societal phenomena. Social physics examines topics at the core of modern human societies and explores potential threats to society. The future of this field looks promising, with physicists playing an important role in studying social phenomena.
PHYSICS REPORTS-REVIEW SECTION OF PHYSICS LETTERS
(2022)
Article
Multidisciplinary Sciences
Boris Podobnik, Marko Jusup, Dean Korosak, Petter Holme, Tomislav Lipic
Summary: This article empirically documents a tipping point in the relationship between democratic norms and corruption suppression, and demonstrates how such a tipping point emerges from a micro-scale mechanistic model. The research suggests that under weak democratic norms, corruption is difficult to suppress, while strong democratic norms can effectively suppress corruption.
PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
(2022)
Article
Public, Environmental & Occupational Health
Fan Fang, Tong Wang, Suoyi Tan, Saran Chen, Tao Zhou, Wei Zhang, Qiang Guo, Jianguo Liu, Petter Holme, Xin Lu
Summary: The study found that during the COVID-19 pandemic, social media networks experienced an increase in size and frequency of interactions. The number of unique recipients, average degree, and transitivity also increased during the severe stage of the outbreak. The similarity of topics discussed on Weibo increased during the local peak of the epidemic, and the number of communities focused on COVID-19 increased significantly. Furthermore, there was a statistically significant rebound effect in the emotional content of users' posts.
FRONTIERS IN PUBLIC HEALTH
(2022)
Article
Engineering, Multidisciplinary
Marcin Waniek, Petter Holme, Talal Rahwan
Summary: Social network analysis tools can infer various attributes by examining connections. Previous studies on hiding personal importance in static networks have overlooked the more general case of temporal networks. This research investigates the concealment of personal importance in temporal networks with changing edges. The study shows that finding the optimal hiding strategy is usually computationally infeasible, but manipulating contacts can increase privacy. Temporal networks provide more strategies for manipulation compared to static networks, making hiding easier.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2022)
Article
Physics, Multidisciplinary
Francesco Picciolo, Franco Ruzzenenti, Petter Holme, Rossana Mastrandrea
Summary: Network theory has proven to be a useful paradigm for understanding the organization and functioning of complex systems in the real world. This study proposes a novel methodology for analyzing weighted motifs and applies it to various real networks. The identified similarities enable the classification of systems based on specific configurations associated with functioning mechanisms.
NEW JOURNAL OF PHYSICS
(2022)
Editorial Material
Multidisciplinary Sciences
Petter Holme
NATURE COMMUNICATIONS
(2022)
Article
Multidisciplinary Sciences
Marcin Waniek, Petter Holme, Manuel Cebrian, Talal Rahwan
Summary: Influence through social networks is fundamental, but identifying the diffusion source is challenging as sources can strategically modify network structure. Efforts should focus on exposing concealed ties rather than planted entities to improve detection.
Article
Multidisciplinary Sciences
Zhizheng Wang, Xiao Fan Liu, Zhanwei Du, Lin Wang, Ye Wu, Petter Holme, Michael Lachmann, Hongfei Lin, Zoie S. Y. Wong, Xiao-Ke Xu, Yuanyuan Sun
Summary: This paper proposes a computational framework that can automatically extract epidemiological information from open-access COVID-19 case reports, and provides an open-access online platform to implement the algorithm.
Article
Multidisciplinary Sciences
Marcin Waniek, Petter Holme, Katayoun Farrahi, Remi Emonet, Manuel Cebrian, Talal Rahwan
Summary: This study shows that increasing the budget for contact tracing improves the identification of infected individuals, but has diminishing returns in terms of source detection. Disease variants with higher infectivity make it easier to find the source, but harder to identify infected individuals. There is a trade-off between using contact tracing to identify patient zero or detect infected individuals.
SCIENTIFIC REPORTS
(2022)
Article
Psychology, Biological
Fengyuan Liu, Petter Holme, Matteo Chiesa, Bedoor AlShebli, Talal Rahwan
Summary: Using algorithmic tools, researchers found pervasive gender inequalities among academic editors, with only 8% of editors-in-chief being women. Men editors were more likely to self-publish in the journal they edit. Career length explains the gender gap among editors, but not editors-in-chief.
NATURE HUMAN BEHAVIOUR
(2023)
Article
Public, Environmental & Occupational Health
Zhanwei Du, Qi Tan, Yuan Bai, Lin Wang, Benjamin J. Cowling, Petter Holme
Summary: The ease of COVID-19 non-pharmacological interventions and the increased susceptibility during the past COVID-19 pandemic may lead to a severe outbreak of influenza in the winter of 2022 and future seasons. The availability of Electronic Health Records (EHR) data in public health systems offers new opportunities to monitor individuals and mitigate outbreaks.
Article
Public, Environmental & Occupational Health
Yuan Bai, Mingda Xu, Caifen Liu, Mingwang Shen, Lin Wang, Linwei Tian, Suoyi Tan, Lei Zhang, Petter Holme, Xin Lu, Eric H. Y. Lau, Benjamin J. Cowling, Zhanwei Du
Summary: This study estimates the importation and exportation risks between Chinese cities using a stochastic metapopulation model based on the epidemic characteristics of COVID-19. It reveals the existence of high exportation risks in certain regions, emphasizing the importance of preparedness and control measures for COVID-19.
Article
Multidisciplinary Sciences
Shupeng Gao, Lili Chang, Ivan Romic, Zhen Wang, Marko Jusup, Petter Holme
Summary: This article presents a solution to the problem of controlling Turing patterns in networks, utilizing an analytical framework and numerical algorithm. The authors demonstrate the effectiveness of their method and discuss factors that impact its performance. They also pave the way for multidisciplinary applications of their framework beyond reaction-diffusion models.
JOURNAL OF THE ROYAL SOCIETY INTERFACE
(2022)
Article
Multidisciplinary Sciences
Zhanwei Du, Yuan Bai, Lin Wang, Jose L. Herrera-Diestra, Zhilu Yuan, Renzhong Guo, Benjamin J. Cowling, Lauren A. Meyers, Petter Holme
Summary: This article proposes a data-driven COVID-19 surveillance strategy using Electronic Health Record (EHR) data to identify vulnerable individuals who are at high risk of early infection. The simulations demonstrate that this strategy performs as well as the most-connected strategy and can detect early warning signals and peak timings earlier than random acquaintance surveillance.
Article
Social Sciences, Mathematical Methods
Luis E. C. Rocha, Petter Holme, Claudio D. G. Linhares
Summary: This study examines the international migration patterns of sex-workers using network methods. The results reveal that the migration network of sex-workers has different structural patterns compared to the general population. Europe is divided into various network communities with specific connections to non-European countries. Moreover, there are asymmetrical relations between countries, with some predominantly offering services while others attract workers. The financial gains of migration are related to the GDP per capita of the country of origin.
JOURNAL OF COMPUTATIONAL SOCIAL SCIENCE
(2022)