In recent years,there has been a growing interest in graph convolutional networks(GCN).However,existing GCN and variants are predominantly based on simple graph or hypergraph structures,which restricts their ability t...In recent years,there has been a growing interest in graph convolutional networks(GCN).However,existing GCN and variants are predominantly based on simple graph or hypergraph structures,which restricts their ability to handle complex data correlations in practical applications.These limitations stem from the difficulty in establishing multiple hierarchies and acquiring adaptive weights for each of them.To address this issue,this paper introduces the latest concept of complex hypergraphs and constructs a versatile high-order multi-level data correlation model.This model is realized by establishing a three-tier structure of complexes-hypergraphs-vertices.Specifically,we start by establishing hyperedge clusters on a foundational network,utilizing a second-order hypergraph structure to depict potential correlations.For this second-order structure,truncation methods are used to assess and generate a three-layer composite structure.During the construction of the composite structure,an adaptive learning strategy is implemented to merge correlations across different levels.We evaluate this model on several popular datasets and compare it with recent state-of-the-art methods.The comprehensive assessment results demonstrate that the proposed model surpasses the existing methods,particularly in modeling implicit data correlations(the classification accuracy of nodes on five public datasets Cora,Citeseer,Pubmed,Github Web ML,and Facebook are 86.1±0.33,79.2±0.35,83.1±0.46,83.8±0.23,and 80.1±0.37,respectively).This indicates that our approach possesses advantages in handling datasets with implicit multi-level structures.展开更多
The principal circadian clock in the suprachiasm nucleus （SCN） regulates the circadian rhythm of physiological and behavioral activities of mammals. Except for the normal function of the circadian rhythm, the ensemb...The principal circadian clock in the suprachiasm nucleus （SCN） regulates the circadian rhythm of physiological and behavioral activities of mammals. Except for the normal function of the circadian rhythm, the ensemble of SCN neurons may show two collective behaviors, i.e., a free running period in the absence of a light-dark cycle and an entrainment ability to an external T cycle. Experiments show that both the free running periods and the entrainment ranges may vary from one species to another and can be seriously influenced by the coupling among the SCN neurons. We here review the recent progress on how the heterogeneous couplings influence these two collective behaviors. We will show that in the case of homogeneous coupling, the free running period increases monotonically while the entrainment range decreases monotonically with the increase of the coupling strength. While in the case of heterogenous coupling, the dispersion of the coupling strength plays a crucial role. It has been found that the free running period decreases with the increase of the dispersion while the entrainment ability is enhanced by the dispersion. These findings provide new insights into the mechanism of the circadian clock in the SCN.展开更多
Adaption of circadian rhythms in behavioral and physiological activities to the external light–dark cycle is achieved through the main clock, i.e., the suprachiasmatic nucleus(SCN) of the brain in mammals. It has bee...Adaption of circadian rhythms in behavioral and physiological activities to the external light–dark cycle is achieved through the main clock, i.e., the suprachiasmatic nucleus(SCN) of the brain in mammals. It has been found that the SCN neurons differ in the amplitude relaxation rate, which represents the rigidity of the neurons to the external amplitude disturbance. Thus far, the appearance of that difference has not been explained. In the present study, an alternative explanation based on the Poincare′ model is given which takes into account the effect of the difference in the entrainment range of the SCN. Both our simulation results and theoretical analyses show that the largest entrainment range is obtained with suitable difference in the case that only a part of SCN neurons are sensitive to the light information. Our findings may give an alternative explanation for the appearance of that difference(heterogeneity) and shed light on the effects of the heterogeneity in the neuronal properties on the collective behaviors of the SCN neurons.展开更多
Projection is a widely used method in bipartite networks. However, each projection has a specific application scenario and differs in the forms of mapping for bipartite networks. In this paper, inspired by the network...Projection is a widely used method in bipartite networks. However, each projection has a specific application scenario and differs in the forms of mapping for bipartite networks. In this paper, inspired by the network-based information exchange dynamics, we propose a uniform framework of projection. Subsequently, an information exchange rate projection based on the nature of community structures of a network （named IERCP） is designed to detect community structures of bipartite networks. Results from the synthetic and real-world networks show that the IERCP algorithm has higher performance compared with the other projection methods. It suggests that the IERCP may extract more information hidden in bipartite networks and minimize information loss.展开更多
Removal of spiral waves in cardiac muscle is necessary because of their threat to life.Common methods for this removal are to apply a local disturbance to the media,such as a periodic forcing.However,most of these met...Removal of spiral waves in cardiac muscle is necessary because of their threat to life.Common methods for this removal are to apply a local disturbance to the media,such as a periodic forcing.However,most of these methods accelerate the beating of the cardiac muscle,resulting in the aggravation of the ventricular tachycardia,which directly threatens life.In the present study,in order to clear off spiral waves,a global pulse-disturbance is applied to the media based on three models of cardiac muscle.It is found that the spiral waves are eliminated and the frequency of the cardiac muscle is decreased in a short time,and finally,the state of the medium reaches the normal oscillation,which supports a target waves.Our method sheds light on the removal of spiral waves in cardiac muscle and can prevent the ventricular tachycardia as well as the ventricular fibrillation.展开更多
Exposed to the natural light-dark cycle,24 h rhythms exist in behavioral and physiological processes of living beings.Interestingly,under constant darkness or constant light,living beings can maintain a robust endogen...Exposed to the natural light-dark cycle,24 h rhythms exist in behavioral and physiological processes of living beings.Interestingly,under constant darkness or constant light,living beings can maintain a robust endogenous rhythm with a free running period(FRP)close to 24 h.In mammals,the circadian rhythm is coordinated by a master clock located in the suprachiasmatic nucleus(SCN)of the brain,which is composed of about twenty thousand self-oscillating neurons.These SCN neurons form a heterogenous network to output a robust rhythm.Thus far,the exact network topology of the SCN neurons is unknown.In this article,we examine the effect of the SCN network structure on the FRP when exposed to constant light by a Poincare model.Four typical network structures are considered,including a nearest-neighbor coupled network,a Newman-Watts small world network,an Erd¨os-Renyi random network and a Barabasi-Albert(BA)scale free network.The results show that the FRP is longest in the BA network,because the BA network is characterized by the most heterogeneous structure among these four types of networks.These findings are not affected by the average node degree of the SCN network or the value of relaxation rate of the SCN neuronal oscillators.Our findings contribute to the understanding of how the network structure of the SCN neurons influences the FRP.展开更多
Stock markets in the world are linked by complicated and dynamical relationships into a temporal network.Extensive works have provided us with rich findings from the topological properties and their evolutionary traje...Stock markets in the world are linked by complicated and dynamical relationships into a temporal network.Extensive works have provided us with rich findings from the topological properties and their evolutionary trajectories,but the underlying dynamical mechanism is still not in order.In the present work,we proposed a technical scheme to reveal the dynamical law from the temporal network.The index records for the global stock markets form a multivariate time series.One separates the series into segments and calculates the information flows between the markets,resulting in a temporal market network representing the state and its evolution.Then the technique of the Koopman decomposition operator is adopted to find the law stored in the information flows.The results show that the stock market system has a high flexibility,i.e.,it jumps easily between different states.The information flows mainly from high to low volatility stock markets.And the dynamical process of information flow is composed of many dynamic modes distribute homogenously in a wide range of periods from one month to several ten years,but there exist only nine modes dominating the macroscopic patterns.展开更多
A complex system contains generally many elements that are networked by their couplings.The time series of output records of the system's dynamical process is subsequently a cooperative result of the couplings.Dis...A complex system contains generally many elements that are networked by their couplings.The time series of output records of the system's dynamical process is subsequently a cooperative result of the couplings.Discovering the coupling structure stored in the time series is an essential task in time series analysis.However,in the currently used methods for time series analysis the structural information is merged completely by the procedure of statistical average.We propose a concept called mode network to preserve the structural information.Firstly,a time series is decomposed into intrinsic mode functions and residue by means of the empirical mode decomposition solution.The mode functions are employed to represent the contributions from different elements of the system.Each mode function is regarded as a mono-variate time series.All the mode functions form a multivariate time series.Secondly,the co-occurrences between all the mode functions are then used to construct a threshold network(mode network)to display the coupling structure.This method is illustrated by investigating gait time series.It is found that a walk trial can be separated into three stages.In the beginning stage,the residue component dominates the series,which is replaced by the mode function numbered M14 with peaks covering^680 strides(~12 min)in the second stage.In the final stage more and more mode functions join into the backbone.The changes of coupling structure are mainly induced by the co-occurrent strengths of the mode functions numbered as M11,M12,M13,and M14,with peaks covering 200-700 strides.Hence,the mode network can display the rich and dynamical patterns of the coupling structure.This approach can be extended to investigate other complex systems such as the oil price and the stock market price series.展开更多
基金Project supported by the National Natural Science Foundation of China(Grant Nos.12275179 and 11875042)the Natural Science Foundation of Shanghai Municipality,China(Grant No.21ZR1443900)。
文摘In recent years,there has been a growing interest in graph convolutional networks(GCN).However,existing GCN and variants are predominantly based on simple graph or hypergraph structures,which restricts their ability to handle complex data correlations in practical applications.These limitations stem from the difficulty in establishing multiple hierarchies and acquiring adaptive weights for each of them.To address this issue,this paper introduces the latest concept of complex hypergraphs and constructs a versatile high-order multi-level data correlation model.This model is realized by establishing a three-tier structure of complexes-hypergraphs-vertices.Specifically,we start by establishing hyperedge clusters on a foundational network,utilizing a second-order hypergraph structure to depict potential correlations.For this second-order structure,truncation methods are used to assess and generate a three-layer composite structure.During the construction of the composite structure,an adaptive learning strategy is implemented to merge correlations across different levels.We evaluate this model on several popular datasets and compare it with recent state-of-the-art methods.The comprehensive assessment results demonstrate that the proposed model surpasses the existing methods,particularly in modeling implicit data correlations(the classification accuracy of nodes on five public datasets Cora,Citeseer,Pubmed,Github Web ML,and Facebook are 86.1±0.33,79.2±0.35,83.1±0.46,83.8±0.23,and 80.1±0.37,respectively).This indicates that our approach possesses advantages in handling datasets with implicit multi-level structures.
基金supported by the National Natural Science Foundation of China (Grant Nos. 11135001 and 11375066)the Joriss Project,China (Grant No. 78230050)the National Basic Research Program of China (Grant No. 2013CB834100)
文摘The principal circadian clock in the suprachiasm nucleus （SCN） regulates the circadian rhythm of physiological and behavioral activities of mammals. Except for the normal function of the circadian rhythm, the ensemble of SCN neurons may show two collective behaviors, i.e., a free running period in the absence of a light-dark cycle and an entrainment ability to an external T cycle. Experiments show that both the free running periods and the entrainment ranges may vary from one species to another and can be seriously influenced by the coupling among the SCN neurons. We here review the recent progress on how the heterogeneous couplings influence these two collective behaviors. We will show that in the case of homogeneous coupling, the free running period increases monotonically while the entrainment range decreases monotonically with the increase of the coupling strength. While in the case of heterogenous coupling, the dispersion of the coupling strength plays a crucial role. It has been found that the free running period decreases with the increase of the dispersion while the entrainment ability is enhanced by the dispersion. These findings provide new insights into the mechanism of the circadian clock in the SCN.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11875042,11505114,and 10975099)the Program for Professor of Special Appointment(Orientational Scholar)at Shanghai Institutions of Higher Learning,China(Grant Nos.QD2015016 and D-USST02)
文摘Adaption of circadian rhythms in behavioral and physiological activities to the external light–dark cycle is achieved through the main clock, i.e., the suprachiasmatic nucleus(SCN) of the brain in mammals. It has been found that the SCN neurons differ in the amplitude relaxation rate, which represents the rigidity of the neurons to the external amplitude disturbance. Thus far, the appearance of that difference has not been explained. In the present study, an alternative explanation based on the Poincare′ model is given which takes into account the effect of the difference in the entrainment range of the SCN. Both our simulation results and theoretical analyses show that the largest entrainment range is obtained with suitable difference in the case that only a part of SCN neurons are sensitive to the light information. Our findings may give an alternative explanation for the appearance of that difference(heterogeneity) and shed light on the effects of the heterogeneity in the neuronal properties on the collective behaviors of the SCN neurons.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11505114 and 10975099)the Program for Professor of Special Appointment(Orientational Scholar)at Shanghai Institutions of Higher Learning(Grant Nos.QD02015016 and DUSST02)+1 种基金the Shanghai Project for Construction of Discipline Peaks,the Natural Science Foundation of Guangxi Zhuang Guangxi Zhuang Autonomous Region(Grant No.2016GXNSFDA380031)the Fundamental Ability Enhancement Project for Young and Middle-aged University Teachers in Guangxi Zhuang Autonomous Region(Grant No.2017KY0859)
文摘Projection is a widely used method in bipartite networks. However, each projection has a specific application scenario and differs in the forms of mapping for bipartite networks. In this paper, inspired by the network-based information exchange dynamics, we propose a uniform framework of projection. Subsequently, an information exchange rate projection based on the nature of community structures of a network （named IERCP） is designed to detect community structures of bipartite networks. Results from the synthetic and real-world networks show that the IERCP algorithm has higher performance compared with the other projection methods. It suggests that the IERCP may extract more information hidden in bipartite networks and minimize information loss.
基金the National Natural Science Foundation of China(Grant Nos.11875042 and 11505114)the Shanghai project for construction of top disciplines(Grant No.USST-SYS01)。
文摘Removal of spiral waves in cardiac muscle is necessary because of their threat to life.Common methods for this removal are to apply a local disturbance to the media,such as a periodic forcing.However,most of these methods accelerate the beating of the cardiac muscle,resulting in the aggravation of the ventricular tachycardia,which directly threatens life.In the present study,in order to clear off spiral waves,a global pulse-disturbance is applied to the media based on three models of cardiac muscle.It is found that the spiral waves are eliminated and the frequency of the cardiac muscle is decreased in a short time,and finally,the state of the medium reaches the normal oscillation,which supports a target waves.Our method sheds light on the removal of spiral waves in cardiac muscle and can prevent the ventricular tachycardia as well as the ventricular fibrillation.
基金the National Natural Science Foundation of China(Grant Nos.12275179 and 11875042)the Natural Science Foundation of Shanghai(Grant No.21ZR1443900)。
文摘Exposed to the natural light-dark cycle,24 h rhythms exist in behavioral and physiological processes of living beings.Interestingly,under constant darkness or constant light,living beings can maintain a robust endogenous rhythm with a free running period(FRP)close to 24 h.In mammals,the circadian rhythm is coordinated by a master clock located in the suprachiasmatic nucleus(SCN)of the brain,which is composed of about twenty thousand self-oscillating neurons.These SCN neurons form a heterogenous network to output a robust rhythm.Thus far,the exact network topology of the SCN neurons is unknown.In this article,we examine the effect of the SCN network structure on the FRP when exposed to constant light by a Poincare model.Four typical network structures are considered,including a nearest-neighbor coupled network,a Newman-Watts small world network,an Erd¨os-Renyi random network and a Barabasi-Albert(BA)scale free network.The results show that the FRP is longest in the BA network,because the BA network is characterized by the most heterogeneous structure among these four types of networks.These findings are not affected by the average node degree of the SCN network or the value of relaxation rate of the SCN neuronal oscillators.Our findings contribute to the understanding of how the network structure of the SCN neurons influences the FRP.
基金the National Nature Science Foundation of China(Grant Nos.11875042 and 11505114)the Orientational Scholar Program Sponsored by the Shanghai Education Commission,China(Grant Nos.D-USST02 and QD2015016)the Shanghai Project for Construction of Top Disciplines,China(Grant No.USST-SYS-01).
文摘Stock markets in the world are linked by complicated and dynamical relationships into a temporal network.Extensive works have provided us with rich findings from the topological properties and their evolutionary trajectories,but the underlying dynamical mechanism is still not in order.In the present work,we proposed a technical scheme to reveal the dynamical law from the temporal network.The index records for the global stock markets form a multivariate time series.One separates the series into segments and calculates the information flows between the markets,resulting in a temporal market network representing the state and its evolution.Then the technique of the Koopman decomposition operator is adopted to find the law stored in the information flows.The results show that the stock market system has a high flexibility,i.e.,it jumps easily between different states.The information flows mainly from high to low volatility stock markets.And the dynamical process of information flow is composed of many dynamic modes distribute homogenously in a wide range of periods from one month to several ten years,but there exist only nine modes dominating the macroscopic patterns.
基金the National Natural Science Foundation of China(Grant Nos.11805128,11875042,11505114,and 10975099)the Program for Professor of Special Appointment(Orientational Scholar)at Shanghai Institutions of Higher Learning,China(Grant Nos.D-USST02 and QD2015016)the Shanghai Project for Construction of Top Disciplines,China(Grant No.USST-SYS-01).
文摘A complex system contains generally many elements that are networked by their couplings.The time series of output records of the system's dynamical process is subsequently a cooperative result of the couplings.Discovering the coupling structure stored in the time series is an essential task in time series analysis.However,in the currently used methods for time series analysis the structural information is merged completely by the procedure of statistical average.We propose a concept called mode network to preserve the structural information.Firstly,a time series is decomposed into intrinsic mode functions and residue by means of the empirical mode decomposition solution.The mode functions are employed to represent the contributions from different elements of the system.Each mode function is regarded as a mono-variate time series.All the mode functions form a multivariate time series.Secondly,the co-occurrences between all the mode functions are then used to construct a threshold network(mode network)to display the coupling structure.This method is illustrated by investigating gait time series.It is found that a walk trial can be separated into three stages.In the beginning stage,the residue component dominates the series,which is replaced by the mode function numbered M14 with peaks covering^680 strides(~12 min)in the second stage.In the final stage more and more mode functions join into the backbone.The changes of coupling structure are mainly induced by the co-occurrent strengths of the mode functions numbered as M11,M12,M13,and M14,with peaks covering 200-700 strides.Hence,the mode network can display the rich and dynamical patterns of the coupling structure.This approach can be extended to investigate other complex systems such as the oil price and the stock market price series.
