Identifying critical nodes is a pivotal research topic in network science,yet the efficient and accurate detection of highly influential nodes remains a challenge.Existing centrality measures predominantly rely on loc...Identifying critical nodes is a pivotal research topic in network science,yet the efficient and accurate detection of highly influential nodes remains a challenge.Existing centrality measures predominantly rely on local or global topological structures,often overlooking indirect connections and their interaction strengths.This leads to imprecise assessments of node importance,limiting practical applications.To address this,we propose a novel node centrality measure,termed six-degree gravity centrality(SDGC),grounded in the six degrees of separation theory,for the precise identification of influential nodes in networks.Specifically,we introduce a set of node influence parameters—node mass,dynamic interaction distance,and attraction coefficient—to enhance the gravity model.Node mass is calculated by integrating K-shell and closeness centrality measures.The dynamic interaction distance,informed by the six-degrees of separation theory,is determined through path searches within six hops between node pairs.The attraction coefficient is derived from the difference in K-shell values between nodes.By integrating these parameters,we develop an improved gravity model to quantify node influence.Experiments conducted on nine real-world networks demonstrate that SDGC significantly outperforms nine existing classical and state-of-the-art methods in identifying the influential nodes.展开更多
Predicting cross-immunity between viral strains is vital for public health surveillance and vaccine development.Traditional neural network methods,such as BiLSTM,could be ineffective due to the lack of lab data for mo...Predicting cross-immunity between viral strains is vital for public health surveillance and vaccine development.Traditional neural network methods,such as BiLSTM,could be ineffective due to the lack of lab data for model training and the overshadowing of crucial features within sequence concatenation.The current work proposes a less data-consuming model incorporating a pre-trained gene sequence model and a mutual information inference operator.Our methodology utilizes gene alignment and deduplication algorithms to preprocess gene sequences,enhancing the model’s capacity to discern and focus on distinctions among input gene pairs.The model,i.e.,DNA Pretrained Cross-Immunity Protection Inference model(DPCIPI),outperforms state-of-theart(SOTA)models in predicting hemagglutination inhibition titer from influenza viral gene sequences only.Improvement in binary cross-immunity prediction is 1.58%in F1,2.34%in precision,1.57%in recall,and 1.57%in Accuracy.For multilevel cross-immunity improvements,the improvement is 2.12%in F1,3.50%in precision,2.19%in recall,and 2.19%in Accuracy.Our study showcases the potential of pre-trained gene models to improve predictions of antigenic variation and cross-immunity.With expanding gene data and advancements in pre-trained models,this approach promises significant impacts on vaccine development and public health.展开更多
In recent years, clapping synchronization between individuals has been widely studied as one of the typical synchronization phenomena. In this paper, we aim to reveal the synchronization mechanism of clapping interact...In recent years, clapping synchronization between individuals has been widely studied as one of the typical synchronization phenomena. In this paper, we aim to reveal the synchronization mechanism of clapping interactions by observing two individuals’ clapping rhythms in a series of experiments. We find that the two synchronizing clapping rhythm series exhibit long-range cross-correlations(LRCCs);that is, the interaction of clapping rhythms can be seen as a strong-anticipation process. Previous studies have demonstrated that the interactions in local timescales or global matching in statistical structures of fluctuation in long timescales can be sources of the strong-anticipation process. However, the origin of the strong anticipation process often appears elusive in many complex systems. Here, we find that the clapping synchronization process may result from the local interaction between two clapping individuals and may result from the more global coordination between two clapping individuals. We introduce two stochastic models for mutually interacting clapping individuals that generate the LRCCs and prove theoretically that the generation of clapping synchronization process needs to consider both local interaction and global matching. This study provides a statistical framework for studying the internal synchronization mechanism of other complex systems. Our theoretical model can also be applied to study the dynamics of other complex systems with the LRCCs, including finance, transportation, and climate.展开更多
Wearing masks is an easy way to operate and popular measure for preventing epidemics.Although masks can slow down the spread of viruses,their efficacy in gathering environments involving heterogeneous person-to-person...Wearing masks is an easy way to operate and popular measure for preventing epidemics.Although masks can slow down the spread of viruses,their efficacy in gathering environments involving heterogeneous person-to-person contacts remains unknown.Therefore,we aim to investigate the epidemic prevention effect of masks in different real-life gathering environments.This study uses four real interpersonal contact datasets to construct four empirical networks to represent four gathering environments.The transmission of COVID-19 is simulated using the Monte Carlo simulation method.The heterogeneity of individuals can cause mask efficacy in a specific gathering environment to be different from the baseline efficacy in general society.Furthermore,the heterogeneity of gathering environments causes the epidemic prevention effect of masks to differ.Wearing masks can greatly reduce the probability of clustered epidemics and the infection scale in primary schools,high schools,and hospitals.However,the use of masks alone in primary schools and hospitals cannot control outbreaks.In high schools with social distancing between classes and in workplaces where the interpersonal contact is relatively sparse,masks can meet the need for prevention.Given the heterogeneity of individual behavior,if individuals who are more active in terms of interpersonal contact are prioritized for mask-wearing,the epidemic prevention effect of masks can be improved.Finally,asymptomatic infection has varying effects on the prevention effect of masks in different environments.The effect can be weakened or eliminated by increasing the usage rate of masks in high schools and workplaces.However,the effect on primary schools and hospitals cannot be weakened.This study contributes to the accurate evaluation of mask efficacy in various gathering environments to provide scientific guidance for epidemic prevention.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.62173065)the Natural Science Foundation of Beijing(Grant No.4242040)+1 种基金the Intelligent Policing and National Security Risk Management Laboratory Open Topics for the year 2025(Grant No.ZHKFYB2503)the Intelligent Policing and National Security Risk Management Laboratory Open Topics for the year 2024(Grant No.ZHKFZD2401).
文摘Identifying critical nodes is a pivotal research topic in network science,yet the efficient and accurate detection of highly influential nodes remains a challenge.Existing centrality measures predominantly rely on local or global topological structures,often overlooking indirect connections and their interaction strengths.This leads to imprecise assessments of node importance,limiting practical applications.To address this,we propose a novel node centrality measure,termed six-degree gravity centrality(SDGC),grounded in the six degrees of separation theory,for the precise identification of influential nodes in networks.Specifically,we introduce a set of node influence parameters—node mass,dynamic interaction distance,and attraction coefficient—to enhance the gravity model.Node mass is calculated by integrating K-shell and closeness centrality measures.The dynamic interaction distance,informed by the six-degrees of separation theory,is determined through path searches within six hops between node pairs.The attraction coefficient is derived from the difference in K-shell values between nodes.By integrating these parameters,we develop an improved gravity model to quantify node influence.Experiments conducted on nine real-world networks demonstrate that SDGC significantly outperforms nine existing classical and state-of-the-art methods in identifying the influential nodes.
基金supported by the Bill & Melinda Gates Foundation and the Minderoo Foundation
文摘Predicting cross-immunity between viral strains is vital for public health surveillance and vaccine development.Traditional neural network methods,such as BiLSTM,could be ineffective due to the lack of lab data for model training and the overshadowing of crucial features within sequence concatenation.The current work proposes a less data-consuming model incorporating a pre-trained gene sequence model and a mutual information inference operator.Our methodology utilizes gene alignment and deduplication algorithms to preprocess gene sequences,enhancing the model’s capacity to discern and focus on distinctions among input gene pairs.The model,i.e.,DNA Pretrained Cross-Immunity Protection Inference model(DPCIPI),outperforms state-of-theart(SOTA)models in predicting hemagglutination inhibition titer from influenza viral gene sequences only.Improvement in binary cross-immunity prediction is 1.58%in F1,2.34%in precision,1.57%in recall,and 1.57%in Accuracy.For multilevel cross-immunity improvements,the improvement is 2.12%in F1,3.50%in precision,2.19%in recall,and 2.19%in Accuracy.Our study showcases the potential of pre-trained gene models to improve predictions of antigenic variation and cross-immunity.With expanding gene data and advancements in pre-trained models,this approach promises significant impacts on vaccine development and public health.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11765008,71731002,and 11775034)the Jiangxi Provincial Natural Science Foundation,China(Grant No.20202ACBL201004)。
文摘In recent years, clapping synchronization between individuals has been widely studied as one of the typical synchronization phenomena. In this paper, we aim to reveal the synchronization mechanism of clapping interactions by observing two individuals’ clapping rhythms in a series of experiments. We find that the two synchronizing clapping rhythm series exhibit long-range cross-correlations(LRCCs);that is, the interaction of clapping rhythms can be seen as a strong-anticipation process. Previous studies have demonstrated that the interactions in local timescales or global matching in statistical structures of fluctuation in long timescales can be sources of the strong-anticipation process. However, the origin of the strong anticipation process often appears elusive in many complex systems. Here, we find that the clapping synchronization process may result from the local interaction between two clapping individuals and may result from the more global coordination between two clapping individuals. We introduce two stochastic models for mutually interacting clapping individuals that generate the LRCCs and prove theoretically that the generation of clapping synchronization process needs to consider both local interaction and global matching. This study provides a statistical framework for studying the internal synchronization mechanism of other complex systems. Our theoretical model can also be applied to study the dynamics of other complex systems with the LRCCs, including finance, transportation, and climate.
基金This work was supported by the National Natural Science Foundation of China(Grant Nos.62173065,11875005,61976025,and 11975025)the University Synergy Innovation Program of Anhui Province(Grant No.GXXT-2021-032)+1 种基金the Natural Science Foundation of Liaoning Province(Grant No.2020-MZLH-22)Major Project of the National Social Science Fund of China(Grant No.19ZDA324).
文摘Wearing masks is an easy way to operate and popular measure for preventing epidemics.Although masks can slow down the spread of viruses,their efficacy in gathering environments involving heterogeneous person-to-person contacts remains unknown.Therefore,we aim to investigate the epidemic prevention effect of masks in different real-life gathering environments.This study uses four real interpersonal contact datasets to construct four empirical networks to represent four gathering environments.The transmission of COVID-19 is simulated using the Monte Carlo simulation method.The heterogeneity of individuals can cause mask efficacy in a specific gathering environment to be different from the baseline efficacy in general society.Furthermore,the heterogeneity of gathering environments causes the epidemic prevention effect of masks to differ.Wearing masks can greatly reduce the probability of clustered epidemics and the infection scale in primary schools,high schools,and hospitals.However,the use of masks alone in primary schools and hospitals cannot control outbreaks.In high schools with social distancing between classes and in workplaces where the interpersonal contact is relatively sparse,masks can meet the need for prevention.Given the heterogeneity of individual behavior,if individuals who are more active in terms of interpersonal contact are prioritized for mask-wearing,the epidemic prevention effect of masks can be improved.Finally,asymptomatic infection has varying effects on the prevention effect of masks in different environments.The effect can be weakened or eliminated by increasing the usage rate of masks in high schools and workplaces.However,the effect on primary schools and hospitals cannot be weakened.This study contributes to the accurate evaluation of mask efficacy in various gathering environments to provide scientific guidance for epidemic prevention.
