Lifelines are critical infrastructure systems characterized by a high level of interdependency that can lead to cascading failures after any disaster.Many approaches can be used to analyze infrastructural interdepende...Lifelines are critical infrastructure systems characterized by a high level of interdependency that can lead to cascading failures after any disaster.Many approaches can be used to analyze infrastructural interdependencies,but they are usually not able to describe the sequence of events during emergencies.Therefore,interdependencies need to be modeled also taking into account the time effects.The methodology proposed in this paper is based on a modified version of the Input-output Inoperability Model and returns the probabilities of failure for each node of the system.Lifelines are modeled using graph theory,while perturbations,representing a natural or man-made disaster,are applied to the elements of the network following predetermined rules.The cascading effects among interdependent networks have been simulated using a spatial multilayer approach,while the use of an adjacency tensor allows to consider the temporal dimension and its effects.The method has been tested on a case study based on the 2011 Fukushima Dai-ichi nuclear disaster.Different configurations of the system have been analyzed and their probability of occurrence evaluated.Two models of the nuclear power plant have been developed to evaluate how different spatial scales and levels of detail affect the results.展开更多
The shortest path is a widely studied network science problem and has attracted great attention.Nevertheless,it draws little attention in temporal networks,in which temporal edges determine information dissemination.I...The shortest path is a widely studied network science problem and has attracted great attention.Nevertheless,it draws little attention in temporal networks,in which temporal edges determine information dissemination.In this paper,we propose an information spreading-based method to calculate the shortest paths distribution in temporal networks.We verify our method on both artificial and real-world temporal networks and obtain a good agreement.We further generalize our method to identify influential nodes and found an effective method.Finally,we verify the influential nodes identifying method on four networks.展开更多
Startups form an information network that reflects their growth trajectories through information flow channels established by shared investors.However,traditional static network metrics overlook temporal dynamics and ...Startups form an information network that reflects their growth trajectories through information flow channels established by shared investors.However,traditional static network metrics overlook temporal dynamics and rely on single indicators to assess startups’roles in predicting future success,failing to comprehensively capture topological variations and structural diversity.To address these limitations,we construct a temporal information network using 14547 investment records from 1013 global blockchain startups between 2004 and 2020,sourced from Crunchbase.We propose two dynamic methods to characterize the information flow:temporal random walk(sTRW)for modeling information flow trajectories and temporal betweenness centrality(tTBET)for identifying key information hubs.These methods enhance walk coverage while ensuring random stability,allowing for more effective identification of influential startups.By integrating sTRW and tTBET,we develop a comprehensive metric to evaluate a startup’s influence within the network.In experiments assessing startups’potential for future success—where successful startups are defined as those that have undergone M&A or IPO—incorporating this metric improves accuracy,recall,and F1 score by 0.035,0.035,and 0.042,respectively.Our findings indicate that information flow from key startups to others diminishes as the network distance increases.Additionally,successful startups generally exhibit higher information inflows than outflows,suggesting that actively seeking investment-related information contributes to startup growth.Our research provides valuable insights for formulating startup development strategies and offers practical guidance for market regulators.展开更多
Essential proteins are an indispensable part of cells and play an extremely significant role in genetic disease diagnosis and drug development.Therefore,the prediction of essential proteins has received extensive atte...Essential proteins are an indispensable part of cells and play an extremely significant role in genetic disease diagnosis and drug development.Therefore,the prediction of essential proteins has received extensive attention from researchers.Many centrality methods and machine learning algorithms have been proposed to predict essential proteins.Nevertheless,the topological characteristics learned by the centrality method are not comprehensive enough,resulting in low accuracy.In addition,machine learning algorithms need sufficient prior knowledge to select features,and the ability to solve imbalanced classification problems needs to be further strengthened.These two factors greatly affect the performance of predicting essential proteins.In this paper,we propose a deep learning framework based on temporal convolutional networks to predict essential proteins by integrating gene expression data and protein-protein interaction(PPI)network.We make use of the method of network embedding to automatically learn more abundant features of proteins in the PPI network.For gene expression data,we treat it as sequence data,and use temporal convolutional networks to extract sequence features.Finally,the two types of features are integrated and put into the multi-layer neural network to complete the final classification task.The performance of our method is evaluated by comparing with seven centrality methods,six machine learning algorithms,and two deep learning models.The results of the experiment show that our method is more effective than the comparison methods for predicting essential proteins.展开更多
Discovering regularities between entities in temporal graphs is vital for many real-world applications(e.g.,social recommendation,emergency event detection,and cyberattack event detection).This paper proposes temporal...Discovering regularities between entities in temporal graphs is vital for many real-world applications(e.g.,social recommendation,emergency event detection,and cyberattack event detection).This paper proposes temporal graph association rules(TGARs)that extend traditional graph-pattern association rules in a static graph by incorporating the unique temporal information and constraints.We introduce quality measures(e.g.,support,confidence,and diversification)to characterize meaningful TGARs that are useful and diversified.In addition,the proposed support metric is an upper bound for alternative metrics,allowing us to guarantee a superset of patterns.We extend conventional confidence measures in terms of maximal occurrences of TGARs.The diversification score strikes a balance between interestingness and diversity.Although the problem is NP-hard,we develop an effective discovery algorithm for TGARs that integrates TGARs generation and TGARs selection and shows that mining TGARs is feasible over a temporal graph.We propose pruning strategies to filter TGARs that have low support or cannot make top-k as early as possible.Moreover,we design an auxiliary data structure to prune the TGARs that do not meet the constraints during the TGARs generation process to avoid conducting repeated subgraph matching for each extension in the search space.We experimentally verify the effectiveness,efficiency,and scalability of our algorithms in discovering diversified top-k TGARs from temporal graphs in real-life applications.展开更多
Influence maximization of temporal social networks(IMT)is a problem that aims to find the most influential set of nodes in the temporal network so that their information can be the most widely spread.To solve the IMT ...Influence maximization of temporal social networks(IMT)is a problem that aims to find the most influential set of nodes in the temporal network so that their information can be the most widely spread.To solve the IMT problem,we propose an influence maximization algorithm based on an improved K-shell method,namely improved K-shell in temporal social networks(KT).The algorithm takes into account the global and local structures of temporal social networks.First,to obtain the kernel value Ks of each node,in the global scope,it layers the network according to the temporal characteristic of nodes by improving the K-shell method.Then,in the local scope,the calculation method of comprehensive degree is proposed to weigh the influence of nodes.Finally,the node with the highest comprehensive degree in each core layer is selected as the seed.However,the seed selection strategy of KT can easily lose some influential nodes.Thus,by optimizing the seed selection strategy,this paper proposes an efficient heuristic algorithm called improved K-shell in temporal social networks for influence maximization(KTIM).According to the hierarchical distribution of cores,the algorithm adds nodes near the central core to the candidate seed set.It then searches for seeds in the candidate seed set according to the comprehensive degree.Experiments showthatKTIMis close to the best performing improved method for influence maximization of temporal graph(IMIT)algorithm in terms of effectiveness,but runs at least an order of magnitude faster than it.Therefore,considering the effectiveness and efficiency simultaneously in temporal social networks,the KTIM algorithm works better than other baseline algorithms.展开更多
The ever-growing available visual data(i.e.,uploaded videos and pictures by internet users)has attracted the research community’s attention in the computer vision field.Therefore,finding efficient solutions to extrac...The ever-growing available visual data(i.e.,uploaded videos and pictures by internet users)has attracted the research community’s attention in the computer vision field.Therefore,finding efficient solutions to extract knowledge from these sources is imperative.Recently,the BlazePose system has been released for skeleton extraction from images oriented to mobile devices.With this skeleton graph representation in place,a Spatial-Temporal Graph Convolutional Network can be implemented to predict the action.We hypothesize that just by changing the skeleton input data for a different set of joints that offers more information about the action of interest,it is possible to increase the performance of the Spatial-Temporal Graph Convolutional Network for HAR tasks.Hence,in this study,we present the first implementation of the BlazePose skeleton topology upon this architecture for action recognition.Moreover,we propose the Enhanced-BlazePose topology that can achieve better results than its predecessor.Additionally,we propose different skeleton detection thresholds that can improve the accuracy performance even further.We reached a top-1 accuracy performance of 40.1%on the Kinetics dataset.For the NTU-RGB+D dataset,we achieved 87.59%and 92.1%accuracy for Cross-Subject and Cross-View evaluation criteria,respectively.展开更多
Influence Maximization(IM)aims to select a seed set of size k in a social network so that information can be spread most widely under a specific information propagation model through this set of nodes.However,most exi...Influence Maximization(IM)aims to select a seed set of size k in a social network so that information can be spread most widely under a specific information propagation model through this set of nodes.However,most existing studies on the IM problem focus on static social network features,while neglecting the features of temporal social networks.To bridge this gap,we focus on node features reflected by their historical interaction behavior in temporal social networks,i.e.,interaction attributes and self-similarity,and incorporate them into the influence maximization algorithm and information propagation model.Firstly,we propose a node feature-aware voting algorithm,called ISVoteRank,for seed nodes selection.Specifically,before voting,the algorithm sets the initial voting ability of nodes in a personalized manner by combining their features.During the voting process,voting weights are set based on the interaction strength between nodes,allowing nodes to vote at different extents and subsequently weakening their voting ability accordingly.The process concludes by selecting the top k nodes with the highest voting scores as seeds,avoiding the inefficiency of iterative seed selection in traditional voting-based algorithms.Secondly,we extend the Independent Cascade(IC)model and propose the Dynamic Independent Cascade(DIC)model,which aims to capture the dynamic features in the information propagation process by combining node features.Finally,experiments demonstrate that the ISVoteRank algorithm has been improved in both effectiveness and efficiency compared to baseline methods,and the influence spread through the DIC model is improved compared to the IC model.展开更多
Temporal networks are an effective way to encode temporal information into graph data losslessly.Finding the bursting cohesive subgraph(BCS),which accumulates its cohesiveness at the fastest rate,is an important probl...Temporal networks are an effective way to encode temporal information into graph data losslessly.Finding the bursting cohesive subgraph(BCS),which accumulates its cohesiveness at the fastest rate,is an important problem in temporal networks.The BCS has a large number of applications,such as representing emergency events in social media,traffic congestion in road networks and epidemic outbreak in communities.Nevertheless,existing methods demand the BCS lasting for a time interval,which neglects the timeliness of the BCS.In this paper,we design an early bursting cohesive subgraph(EBCS)model based on the k-core to enable identifying the burstiness as soon as possible.To find the EBCS,we first construct a time weight graph(TWG)to measure the bursting level by integrating the topological and temporal information.Then,we propose a global search algorithm,called GS-EBCS,which can find the exact EBCS by iteratively removing nodes from the TWG.Further,we propose a local search algorithm,named LS-EBCS,to find the EBCS by first expanding from a seed node until obtaining a candidate k-core and then refining the k-core to the result subgraph in an optimal time complexity.Subsequently,considering the situation that the massive temporal networks cannot be completely put into the memory,we first design an I/O method to build the TWG and then develop I/O efficient global search and local search algorithms,namely I/O-GS and I/O-LS respectively,to find the EBCS under the semi-external model.Extensive experiments,conducted on four real temporal networks,demonstrate the efficiency and effectiveness of our proposed algorithms.For example,on the DBLP dataset,I/O-LS and LS-EBCS have comparable running time,while the maximum memory usage of I/O-LS is only 6.5 MB,which is much smaller than that of LS-EBCS taking 308.7 MB.展开更多
Diabetes,as a chronic disease,is caused by the increase of blood glucose concentration due to pancreatic insulin production failure or insulin resistance in the body.Predicting the change trend of blood glucose level ...Diabetes,as a chronic disease,is caused by the increase of blood glucose concentration due to pancreatic insulin production failure or insulin resistance in the body.Predicting the change trend of blood glucose level in advance brings convenience for prompt treatment,so as to maintain blood glucose level within the recommended levels.Based on the flash glucose monitoring data,we propose a method that combines prophet with temporal convolutional networks(TCN)to achieve good experimental results in predicting patient blood glucose.The proposed model achieves high accuracy in the long-term and short-term prediction of blood glucose,and outperforms other models on the adaptability to non-stationary and detection capability of periodic changes.展开更多
Action recognition has been recognized as an activity in which individuals’behaviour can be observed.Assembling profiles of regular activities such as activities of daily living can support identifying trends in the ...Action recognition has been recognized as an activity in which individuals’behaviour can be observed.Assembling profiles of regular activities such as activities of daily living can support identifying trends in the data during critical events.A skeleton representation of the human body has been proven to be effective for this task.The skeletons are presented in graphs form-like.However,the topology of a graph is not structured like Euclideanbased data.Therefore,a new set of methods to perform the convolution operation upon the skeleton graph is proposed.Our proposal is based on the Spatial Temporal-Graph Convolutional Network(ST-GCN)framework.In this study,we proposed an improved set of label mapping methods for the ST-GCN framework.We introduce three split techniques(full distance split,connection split,and index split)as an alternative approach for the convolution operation.The experiments presented in this study have been trained using two benchmark datasets:NTU-RGB+D and Kinetics to evaluate the performance.Our results indicate that our split techniques outperform the previous partition strategies and aremore stable during training without using the edge importance weighting additional training parameter.Therefore,our proposal can provide a more realistic solution for real-time applications centred on daily living recognition systems activities for indoor environments.展开更多
The integration of the Internet of Things(IoT)into healthcare systems improves patient care,boosts operational efficiency,and contributes to cost-effective healthcare delivery.However,overcoming several associated cha...The integration of the Internet of Things(IoT)into healthcare systems improves patient care,boosts operational efficiency,and contributes to cost-effective healthcare delivery.However,overcoming several associated challenges,such as data security,interoperability,and ethical concerns,is crucial to realizing the full potential of IoT in healthcare.Real-time anomaly detection plays a key role in protecting patient data and maintaining device integrity amidst the additional security risks posed by interconnected systems.In this context,this paper presents a novelmethod for healthcare data privacy analysis.The technique is based on the identification of anomalies in cloud-based Internet of Things(IoT)networks,and it is optimized using explainable artificial intelligence.For anomaly detection,the Radial Boltzmann Gaussian Temporal Fuzzy Network(RBGTFN)is used in the process of doing information privacy analysis for healthcare data.Remora Colony SwarmOptimization is then used to carry out the optimization of the network.The performance of the model in identifying anomalies across a variety of healthcare data is evaluated by an experimental study.This evaluation suggested that themodel measures the accuracy,precision,latency,Quality of Service(QoS),and scalability of themodel.A remarkable 95%precision,93%latency,89%quality of service,98%detection accuracy,and 96%scalability were obtained by the suggested model,as shown by the subsequent findings.展开更多
To enhance the denoising performance of event-based sensors,we introduce a clustering-based temporal deep neural network denoising method(CBTDNN).Firstly,to cluster the sensor output data and obtain the respective clu...To enhance the denoising performance of event-based sensors,we introduce a clustering-based temporal deep neural network denoising method(CBTDNN).Firstly,to cluster the sensor output data and obtain the respective cluster centers,a combination of density-based spatial clustering of applications with noise(DBSCAN)and Kmeans++is utilized.Subsequently,long short-term memory(LSTM)is employed to fit and yield optimized cluster centers with temporal information.Lastly,based on the new cluster centers and denoising ratio,a radius threshold is set,and noise points beyond this threshold are removed.The comprehensive denoising metrics F1_score of CBTDNN have achieved 0.8931,0.7735,and 0.9215 on the traffic sequences dataset,pedestrian detection dataset,and turntable dataset,respectively.And these metrics demonstrate improvements of 49.90%,33.07%,19.31%,and 22.97%compared to four contrastive algorithms,namely nearest neighbor(NNb),nearest neighbor with polarity(NNp),Autoencoder,and multilayer perceptron denoising filter(MLPF).These results demonstrate that the proposed method enhances the denoising performance of event-based sensors.展开更多
Intractable delays occur in air traffic due to the imbalance between ever-increasing air traffic demand and limited airspace capacity.As air traffic is associated with complex air transport systems,delays can be magni...Intractable delays occur in air traffic due to the imbalance between ever-increasing air traffic demand and limited airspace capacity.As air traffic is associated with complex air transport systems,delays can be magnified and propagated throughout these systems,resulting in the emergent behavior known as delay propagation.An understanding of delay propagation dynamics is pertinent to modern air traffic management.In this work,we present a complex network perspective of delay propagation dynamics.Specifically,we model air traffic scenarios using spatial–temporal networks with airports as the nodes.To establish the dynamic edges between the nodes,we develop a delay propagation method and apply it to a given set of air traffic schedules.Based on the constructed spatial-temporal networks,we suggest three metrics-magnitude,severity,and speed-to gauge delay propagation dynamics.To validate the effectiveness of the proposed method,we carry out case studies on domestic flights in the Southeastern Asia region(SAR)and the United States.Experiments demonstrate that the propagation magnitude in terms of the number of flights affected by delay propagation and the amount of propagated delays for the US traffic are respectively five and ten times those of the SAR.Experiments further reveal that the propagation speed for US traffic is eight times faster than that of the SAR.The delay propagation dynamics reveal that about six hub airports in the SAR have significant propagated delays,while the situation in the United States is considerably worse,with a corresponding number of around 16.This work provides a potent tool for tracing the evolution of air traffic delays.展开更多
Air transport systems are highly dynamic at temporal scales from minutes to years.This dynamic behavior not only characterizes the evolution of the system but also affect the system＇s functioning.Understanding the ev...Air transport systems are highly dynamic at temporal scales from minutes to years.This dynamic behavior not only characterizes the evolution of the system but also affect the system＇s functioning.Understanding the evolutionary mechanisms is thus fundamental in order to better design optimal air transport networks that benefits companies,passengers and the environment.In this review,we briefly present and discuss the state-of-the-art on time-evolving air transport networks.We distinguish the structural analysis of sequences of network snapshots,ideal for long-term network evolution（e.g.annual evolution）,and temporal paths,preferred for short-term dynamics（e.g.hourly evolution）.We emphasize that most previous research focused on the first modeling approach（i.e.long-term） whereas only a few studies look at high-resolution temporal paths.We conclude the review highlighting that much research remains to be done,both to apply already available methods and to develop new measures for temporal paths on air transport networks.In particular,we identify that the study of delays,network resilience and optimization of resources（aircraft and crew） are critical topics.展开更多
In this paper,we investigate a spectrumsensing system in the presence of a satellite,where the satellite works as a sensing node.Considering the conventional energy detection method is sensitive to the noise uncertain...In this paper,we investigate a spectrumsensing system in the presence of a satellite,where the satellite works as a sensing node.Considering the conventional energy detection method is sensitive to the noise uncertainty,thus,a temporal convolutional network(TCN)based spectrum-sensing method is designed to eliminate the effect of the noise uncertainty and improve the performance of spectrum sensing,relying on the offline training and the online detection stages.Specifically,in the offline training stage,spectrum data captured by the satellite is sent to the TCN deployed on the gateway for training purpose.Moreover,in the online detection stage,the well trained TCN is utilized to perform real-time spectrum sensing,which can upgrade spectrum-sensing performance by exploiting the temporal features.Additionally,simulation results demonstrate that the proposed method achieves a higher probability of detection than that of the conventional energy detection(ED),the convolutional neural network(CNN),and deep neural network(DNN).Furthermore,the proposed method outperforms the CNN and the DNN in terms of a lower computational complexity.展开更多
In this article, the notion of pinning control for directed networks of dynamical systems is introduced, where the nodes could be either single-input single-output （SISO） or multi-input multi-output （MIMO） dynamic...In this article, the notion of pinning control for directed networks of dynamical systems is introduced, where the nodes could be either single-input single-output （SISO） or multi-input multi-output （MIMO） dynamical systems, and could be non-identical and nonlinear in general but will be specified to be identical linear time-invariant （LTI） systems here in the study of network controllability. Both state and structural controllability problems will be discussed, illustrating how the network topology, node-system dynamics, external control inputs and inner dynamical interactions altogether affect the controllability of a general complex network of LTI systems, with necessary and sufficient conditions presented for both SISO and MIMO settings. To that end, the controllability of a special temporally switching directed network of linear time-varying （LTV） node systems will be addressed, leaving some more general networks and challenging issues to the end for research outlook.展开更多
Since the oil production of single well in water flooding reservoir varies greatly and is hard to predict, an oil production prediction method of single well based on temporal convolutional network(TCN) is proposed an...Since the oil production of single well in water flooding reservoir varies greatly and is hard to predict, an oil production prediction method of single well based on temporal convolutional network(TCN) is proposed and verified. This method is started from data processing, the correspondence between water injectors and oil producers is determined according to the influence radius of the water injectors, the influence degree of a water injector on an oil producer in the month concerned is added as a model feature, and a Random Forest(RF) model is built to fill the dynamic data of water flooding. The single well history is divided into 4 stages according to its water cut, that is, low water cut, middle water cut, high water cut and extra-high water cut stages. In each stage, a TCN based prediction model is established, hyperparameters of the model are optimized by the Sparrow Search Algorithm(SSA). Finally, the models of the 4 stages are integrated into one whole-life model of the well for production prediction. The application of this method in Daqing Oilfield, NE China shows that:(1) Compared with conventional data processing methods, the data obtained by this processing method are more close to the actual production, and the data set obtained is more authentic and complete.(2) The TCN model has higher prediction accuracy than other 11 models such as Long Short Term Memory(LSTM).(3) Compared with the conventional full-life-cycle models, the model of integrated stages can significantly reduce the error of production prediction.展开更多
In order to reduce the physical impairment caused by signal distortion,in this paper,we investigate symbol detection with Deep Learning(DL)methods to improve bit-error performance in the optical communication system.M...In order to reduce the physical impairment caused by signal distortion,in this paper,we investigate symbol detection with Deep Learning(DL)methods to improve bit-error performance in the optical communication system.Many DL-based methods have been applied to such systems to improve bit-error performance.Referring to the speech-to-text method of automatic speech recognition,this paper proposes a signal-to-symbol method based on DL and designs a receiver for symbol detection on single-polarized optical communications modes.To realize this detection method,we propose a non-causal temporal convolutional network-assisted receiver to detect symbols directly from the baseband signal,which specifically integrates most modules of the receiver.Meanwhile,we adopt three training approaches for different signal-to-noise ratios.We also apply a parametric rectified linear unit to enhance the noise robustness of the proposed network.According to the simulation experiments,the biterror-rate performance of the proposed method is close to or even superior to that of the conventional receiver and better than the recurrent neural network-based receiver.展开更多
A lightweight multi-layer residual temporal convolutional network model(RTCN)is proposed to target the highly complex kinematics and temporal correlation of human motion.RTCN uses 1-D convolution to efficiently obtain...A lightweight multi-layer residual temporal convolutional network model(RTCN)is proposed to target the highly complex kinematics and temporal correlation of human motion.RTCN uses 1-D convolution to efficiently obtain the spatial structure information of human motion and extract the correlation in the time series of human motion.The residual structure is applied to the proposed network model to alleviate the problem of gradient disappearance in the deep network.Experiments on the Human 3.6M dataset demonstrate that the proposed method effectively reduces the errors of motion prediction compared with previous methods,especially of long-term prediction.展开更多
基金the European Research Council under the Grant agreement no.ERC_IDEAL RESCUE_637842 of the project IDEAL RESCUE_Integrated Design and Control of Sustainable Communities during Emergencies.
文摘Lifelines are critical infrastructure systems characterized by a high level of interdependency that can lead to cascading failures after any disaster.Many approaches can be used to analyze infrastructural interdependencies,but they are usually not able to describe the sequence of events during emergencies.Therefore,interdependencies need to be modeled also taking into account the time effects.The methodology proposed in this paper is based on a modified version of the Input-output Inoperability Model and returns the probabilities of failure for each node of the system.Lifelines are modeled using graph theory,while perturbations,representing a natural or man-made disaster,are applied to the elements of the network following predetermined rules.The cascading effects among interdependent networks have been simulated using a spatial multilayer approach,while the use of an adjacency tensor allows to consider the temporal dimension and its effects.The method has been tested on a case study based on the 2011 Fukushima Dai-ichi nuclear disaster.Different configurations of the system have been analyzed and their probability of occurrence evaluated.Two models of the nuclear power plant have been developed to evaluate how different spatial scales and levels of detail affect the results.
基金Project supported by the National Natural Science Foundation of China(Grant No.61903266)China Postdoctoral Science Foundation(Grant No.2018M631073)+2 种基金China Postdoctoral Science Special Foundation(Grant No.2019T120829)the Fundamental Research Funds for the Central Universities,ChinaSichuan Science and Technology Program,China(Grant No.20YYJC4001)。
文摘The shortest path is a widely studied network science problem and has attracted great attention.Nevertheless,it draws little attention in temporal networks,in which temporal edges determine information dissemination.In this paper,we propose an information spreading-based method to calculate the shortest paths distribution in temporal networks.We verify our method on both artificial and real-world temporal networks and obtain a good agreement.We further generalize our method to identify influential nodes and found an effective method.Finally,we verify the influential nodes identifying method on four networks.
基金the funding from the National Natural Science Foundation of China(Grant Nos.42001236,71991481,and 71991480)Young Elite Scientist Sponsor-ship Program by Bast(Grant No.BYESS2023413)。
文摘Startups form an information network that reflects their growth trajectories through information flow channels established by shared investors.However,traditional static network metrics overlook temporal dynamics and rely on single indicators to assess startups’roles in predicting future success,failing to comprehensively capture topological variations and structural diversity.To address these limitations,we construct a temporal information network using 14547 investment records from 1013 global blockchain startups between 2004 and 2020,sourced from Crunchbase.We propose two dynamic methods to characterize the information flow:temporal random walk(sTRW)for modeling information flow trajectories and temporal betweenness centrality(tTBET)for identifying key information hubs.These methods enhance walk coverage while ensuring random stability,allowing for more effective identification of influential startups.By integrating sTRW and tTBET,we develop a comprehensive metric to evaluate a startup’s influence within the network.In experiments assessing startups’potential for future success—where successful startups are defined as those that have undergone M&A or IPO—incorporating this metric improves accuracy,recall,and F1 score by 0.035,0.035,and 0.042,respectively.Our findings indicate that information flow from key startups to others diminishes as the network distance increases.Additionally,successful startups generally exhibit higher information inflows than outflows,suggesting that actively seeking investment-related information contributes to startup growth.Our research provides valuable insights for formulating startup development strategies and offers practical guidance for market regulators.
基金the National Natural Science Foundation of China(Nos.11861045 and 62162040)。
文摘Essential proteins are an indispensable part of cells and play an extremely significant role in genetic disease diagnosis and drug development.Therefore,the prediction of essential proteins has received extensive attention from researchers.Many centrality methods and machine learning algorithms have been proposed to predict essential proteins.Nevertheless,the topological characteristics learned by the centrality method are not comprehensive enough,resulting in low accuracy.In addition,machine learning algorithms need sufficient prior knowledge to select features,and the ability to solve imbalanced classification problems needs to be further strengthened.These two factors greatly affect the performance of predicting essential proteins.In this paper,we propose a deep learning framework based on temporal convolutional networks to predict essential proteins by integrating gene expression data and protein-protein interaction(PPI)network.We make use of the method of network embedding to automatically learn more abundant features of proteins in the PPI network.For gene expression data,we treat it as sequence data,and use temporal convolutional networks to extract sequence features.Finally,the two types of features are integrated and put into the multi-layer neural network to complete the final classification task.The performance of our method is evaluated by comparing with seven centrality methods,six machine learning algorithms,and two deep learning models.The results of the experiment show that our method is more effective than the comparison methods for predicting essential proteins.
基金This work was partially supported by the National Key Research and Development Program(No.2018YFB1800203)National Natural Science Foundation of China(No.U19B2024)Postgraduate Scientific Research Innovation Project of Hunan Province(No.CX20210038).
文摘Discovering regularities between entities in temporal graphs is vital for many real-world applications(e.g.,social recommendation,emergency event detection,and cyberattack event detection).This paper proposes temporal graph association rules(TGARs)that extend traditional graph-pattern association rules in a static graph by incorporating the unique temporal information and constraints.We introduce quality measures(e.g.,support,confidence,and diversification)to characterize meaningful TGARs that are useful and diversified.In addition,the proposed support metric is an upper bound for alternative metrics,allowing us to guarantee a superset of patterns.We extend conventional confidence measures in terms of maximal occurrences of TGARs.The diversification score strikes a balance between interestingness and diversity.Although the problem is NP-hard,we develop an effective discovery algorithm for TGARs that integrates TGARs generation and TGARs selection and shows that mining TGARs is feasible over a temporal graph.We propose pruning strategies to filter TGARs that have low support or cannot make top-k as early as possible.Moreover,we design an auxiliary data structure to prune the TGARs that do not meet the constraints during the TGARs generation process to avoid conducting repeated subgraph matching for each extension in the search space.We experimentally verify the effectiveness,efficiency,and scalability of our algorithms in discovering diversified top-k TGARs from temporal graphs in real-life applications.
基金Thiswork is supported by theYouth Science and Technology Innovation Personnel Training Project of Heilongjiang(No.UNPYSCT-2020072)the FundamentalResearch Funds for the Universities of Heilongjiang(Nos.145109217,135509234)+1 种基金the Education Science Fourteenth Five-Year Plan 2021 Project of Heilongjiang(No.GJB1421344)the Innovative Research Projects for Postgraduates of Qiqihar University(No.YJSCX2022048).
文摘Influence maximization of temporal social networks(IMT)is a problem that aims to find the most influential set of nodes in the temporal network so that their information can be the most widely spread.To solve the IMT problem,we propose an influence maximization algorithm based on an improved K-shell method,namely improved K-shell in temporal social networks(KT).The algorithm takes into account the global and local structures of temporal social networks.First,to obtain the kernel value Ks of each node,in the global scope,it layers the network according to the temporal characteristic of nodes by improving the K-shell method.Then,in the local scope,the calculation method of comprehensive degree is proposed to weigh the influence of nodes.Finally,the node with the highest comprehensive degree in each core layer is selected as the seed.However,the seed selection strategy of KT can easily lose some influential nodes.Thus,by optimizing the seed selection strategy,this paper proposes an efficient heuristic algorithm called improved K-shell in temporal social networks for influence maximization(KTIM).According to the hierarchical distribution of cores,the algorithm adds nodes near the central core to the candidate seed set.It then searches for seeds in the candidate seed set according to the comprehensive degree.Experiments showthatKTIMis close to the best performing improved method for influence maximization of temporal graph(IMIT)algorithm in terms of effectiveness,but runs at least an order of magnitude faster than it.Therefore,considering the effectiveness and efficiency simultaneously in temporal social networks,the KTIM algorithm works better than other baseline algorithms.
文摘The ever-growing available visual data(i.e.,uploaded videos and pictures by internet users)has attracted the research community’s attention in the computer vision field.Therefore,finding efficient solutions to extract knowledge from these sources is imperative.Recently,the BlazePose system has been released for skeleton extraction from images oriented to mobile devices.With this skeleton graph representation in place,a Spatial-Temporal Graph Convolutional Network can be implemented to predict the action.We hypothesize that just by changing the skeleton input data for a different set of joints that offers more information about the action of interest,it is possible to increase the performance of the Spatial-Temporal Graph Convolutional Network for HAR tasks.Hence,in this study,we present the first implementation of the BlazePose skeleton topology upon this architecture for action recognition.Moreover,we propose the Enhanced-BlazePose topology that can achieve better results than its predecessor.Additionally,we propose different skeleton detection thresholds that can improve the accuracy performance even further.We reached a top-1 accuracy performance of 40.1%on the Kinetics dataset.For the NTU-RGB+D dataset,we achieved 87.59%and 92.1%accuracy for Cross-Subject and Cross-View evaluation criteria,respectively.
基金supported by the Fundamental Research Funds for the Universities of Heilongjiang(Nos.145109217,135509234)the Youth Science and Technology Innovation Personnel Training Project of Heilongjiang(No.UNPYSCT-2020072)the Innovative Research Projects for Postgraduates of Qiqihar University(No.YJSCX2022048).
文摘Influence Maximization(IM)aims to select a seed set of size k in a social network so that information can be spread most widely under a specific information propagation model through this set of nodes.However,most existing studies on the IM problem focus on static social network features,while neglecting the features of temporal social networks.To bridge this gap,we focus on node features reflected by their historical interaction behavior in temporal social networks,i.e.,interaction attributes and self-similarity,and incorporate them into the influence maximization algorithm and information propagation model.Firstly,we propose a node feature-aware voting algorithm,called ISVoteRank,for seed nodes selection.Specifically,before voting,the algorithm sets the initial voting ability of nodes in a personalized manner by combining their features.During the voting process,voting weights are set based on the interaction strength between nodes,allowing nodes to vote at different extents and subsequently weakening their voting ability accordingly.The process concludes by selecting the top k nodes with the highest voting scores as seeds,avoiding the inefficiency of iterative seed selection in traditional voting-based algorithms.Secondly,we extend the Independent Cascade(IC)model and propose the Dynamic Independent Cascade(DIC)model,which aims to capture the dynamic features in the information propagation process by combining node features.Finally,experiments demonstrate that the ISVoteRank algorithm has been improved in both effectiveness and efficiency compared to baseline methods,and the influence spread through the DIC model is improved compared to the IC model.
基金the National Natural Science Foundation of China under Grant Nos.61902004,61772124,61732003,and 61977001the Project of Beijing Municipal Education Commission under Grant No.KM202010009009+1 种基金Innovative Talents of Higher Education in Liaoning Province under Grant No.LR2020076the Basic Research Operating Funds for National Defense Major Incubation Projects under Grant No.N2116017.
文摘Temporal networks are an effective way to encode temporal information into graph data losslessly.Finding the bursting cohesive subgraph(BCS),which accumulates its cohesiveness at the fastest rate,is an important problem in temporal networks.The BCS has a large number of applications,such as representing emergency events in social media,traffic congestion in road networks and epidemic outbreak in communities.Nevertheless,existing methods demand the BCS lasting for a time interval,which neglects the timeliness of the BCS.In this paper,we design an early bursting cohesive subgraph(EBCS)model based on the k-core to enable identifying the burstiness as soon as possible.To find the EBCS,we first construct a time weight graph(TWG)to measure the bursting level by integrating the topological and temporal information.Then,we propose a global search algorithm,called GS-EBCS,which can find the exact EBCS by iteratively removing nodes from the TWG.Further,we propose a local search algorithm,named LS-EBCS,to find the EBCS by first expanding from a seed node until obtaining a candidate k-core and then refining the k-core to the result subgraph in an optimal time complexity.Subsequently,considering the situation that the massive temporal networks cannot be completely put into the memory,we first design an I/O method to build the TWG and then develop I/O efficient global search and local search algorithms,namely I/O-GS and I/O-LS respectively,to find the EBCS under the semi-external model.Extensive experiments,conducted on four real temporal networks,demonstrate the efficiency and effectiveness of our proposed algorithms.For example,on the DBLP dataset,I/O-LS and LS-EBCS have comparable running time,while the maximum memory usage of I/O-LS is only 6.5 MB,which is much smaller than that of LS-EBCS taking 308.7 MB.
文摘Diabetes,as a chronic disease,is caused by the increase of blood glucose concentration due to pancreatic insulin production failure or insulin resistance in the body.Predicting the change trend of blood glucose level in advance brings convenience for prompt treatment,so as to maintain blood glucose level within the recommended levels.Based on the flash glucose monitoring data,we propose a method that combines prophet with temporal convolutional networks(TCN)to achieve good experimental results in predicting patient blood glucose.The proposed model achieves high accuracy in the long-term and short-term prediction of blood glucose,and outperforms other models on the adaptability to non-stationary and detection capability of periodic changes.
文摘Action recognition has been recognized as an activity in which individuals’behaviour can be observed.Assembling profiles of regular activities such as activities of daily living can support identifying trends in the data during critical events.A skeleton representation of the human body has been proven to be effective for this task.The skeletons are presented in graphs form-like.However,the topology of a graph is not structured like Euclideanbased data.Therefore,a new set of methods to perform the convolution operation upon the skeleton graph is proposed.Our proposal is based on the Spatial Temporal-Graph Convolutional Network(ST-GCN)framework.In this study,we proposed an improved set of label mapping methods for the ST-GCN framework.We introduce three split techniques(full distance split,connection split,and index split)as an alternative approach for the convolution operation.The experiments presented in this study have been trained using two benchmark datasets:NTU-RGB+D and Kinetics to evaluate the performance.Our results indicate that our split techniques outperform the previous partition strategies and aremore stable during training without using the edge importance weighting additional training parameter.Therefore,our proposal can provide a more realistic solution for real-time applications centred on daily living recognition systems activities for indoor environments.
基金funded by Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah under grant No.(RG-6-611-43)the authors,therefore,acknowledge with thanks DSR technical and financial support.
文摘The integration of the Internet of Things(IoT)into healthcare systems improves patient care,boosts operational efficiency,and contributes to cost-effective healthcare delivery.However,overcoming several associated challenges,such as data security,interoperability,and ethical concerns,is crucial to realizing the full potential of IoT in healthcare.Real-time anomaly detection plays a key role in protecting patient data and maintaining device integrity amidst the additional security risks posed by interconnected systems.In this context,this paper presents a novelmethod for healthcare data privacy analysis.The technique is based on the identification of anomalies in cloud-based Internet of Things(IoT)networks,and it is optimized using explainable artificial intelligence.For anomaly detection,the Radial Boltzmann Gaussian Temporal Fuzzy Network(RBGTFN)is used in the process of doing information privacy analysis for healthcare data.Remora Colony SwarmOptimization is then used to carry out the optimization of the network.The performance of the model in identifying anomalies across a variety of healthcare data is evaluated by an experimental study.This evaluation suggested that themodel measures the accuracy,precision,latency,Quality of Service(QoS),and scalability of themodel.A remarkable 95%precision,93%latency,89%quality of service,98%detection accuracy,and 96%scalability were obtained by the suggested model,as shown by the subsequent findings.
基金supported by the National Natural Science Foundation of China(No.62134004).
文摘To enhance the denoising performance of event-based sensors,we introduce a clustering-based temporal deep neural network denoising method(CBTDNN).Firstly,to cluster the sensor output data and obtain the respective cluster centers,a combination of density-based spatial clustering of applications with noise(DBSCAN)and Kmeans++is utilized.Subsequently,long short-term memory(LSTM)is employed to fit and yield optimized cluster centers with temporal information.Lastly,based on the new cluster centers and denoising ratio,a radius threshold is set,and noise points beyond this threshold are removed.The comprehensive denoising metrics F1_score of CBTDNN have achieved 0.8931,0.7735,and 0.9215 on the traffic sequences dataset,pedestrian detection dataset,and turntable dataset,respectively.And these metrics demonstrate improvements of 49.90%,33.07%,19.31%,and 22.97%compared to four contrastive algorithms,namely nearest neighbor(NNb),nearest neighbor with polarity(NNp),Autoencoder,and multilayer perceptron denoising filter(MLPF).These results demonstrate that the proposed method enhances the denoising performance of event-based sensors.
基金This work was supported by SUG Research Grant M4082126.050 by the School of Mechanical and Aerospace Engineering(MAE),Nanyang Technological University(NTU),SingaporeNTU-CAAS Research Grant M4062429.052 by the ATM Research Institute,School of MAE,NTU,Singapore.
文摘Intractable delays occur in air traffic due to the imbalance between ever-increasing air traffic demand and limited airspace capacity.As air traffic is associated with complex air transport systems,delays can be magnified and propagated throughout these systems,resulting in the emergent behavior known as delay propagation.An understanding of delay propagation dynamics is pertinent to modern air traffic management.In this work,we present a complex network perspective of delay propagation dynamics.Specifically,we model air traffic scenarios using spatial–temporal networks with airports as the nodes.To establish the dynamic edges between the nodes,we develop a delay propagation method and apply it to a given set of air traffic schedules.Based on the constructed spatial-temporal networks,we suggest three metrics-magnitude,severity,and speed-to gauge delay propagation dynamics.To validate the effectiveness of the proposed method,we carry out case studies on domestic flights in the Southeastern Asia region(SAR)and the United States.Experiments demonstrate that the propagation magnitude in terms of the number of flights affected by delay propagation and the amount of propagated delays for the US traffic are respectively five and ten times those of the SAR.Experiments further reveal that the propagation speed for US traffic is eight times faster than that of the SAR.The delay propagation dynamics reveal that about six hub airports in the SAR have significant propagated delays,while the situation in the United States is considerably worse,with a corresponding number of around 16.This work provides a potent tool for tracing the evolution of air traffic delays.
基金supported by the Fonds De La Recherche Scientifique-FNRS
文摘Air transport systems are highly dynamic at temporal scales from minutes to years.This dynamic behavior not only characterizes the evolution of the system but also affect the system＇s functioning.Understanding the evolutionary mechanisms is thus fundamental in order to better design optimal air transport networks that benefits companies,passengers and the environment.In this review,we briefly present and discuss the state-of-the-art on time-evolving air transport networks.We distinguish the structural analysis of sequences of network snapshots,ideal for long-term network evolution（e.g.annual evolution）,and temporal paths,preferred for short-term dynamics（e.g.hourly evolution）.We emphasize that most previous research focused on the first modeling approach（i.e.long-term） whereas only a few studies look at high-resolution temporal paths.We conclude the review highlighting that much research remains to be done,both to apply already available methods and to develop new measures for temporal paths on air transport networks.In particular,we identify that the study of delays,network resilience and optimization of resources（aircraft and crew） are critical topics.
基金the National Science Foundation of China (No.91738201, 61971440)the Jiangsu Province Basic Research Project (No.BK20192002)+1 种基金the China Postdoctoral Science Foundation (No.2018M632347)the Natural Science Research of Higher Education Institutions of Jiangsu Province (No.18KJB510030)。
文摘In this paper,we investigate a spectrumsensing system in the presence of a satellite,where the satellite works as a sensing node.Considering the conventional energy detection method is sensitive to the noise uncertainty,thus,a temporal convolutional network(TCN)based spectrum-sensing method is designed to eliminate the effect of the noise uncertainty and improve the performance of spectrum sensing,relying on the offline training and the online detection stages.Specifically,in the offline training stage,spectrum data captured by the satellite is sent to the TCN deployed on the gateway for training purpose.Moreover,in the online detection stage,the well trained TCN is utilized to perform real-time spectrum sensing,which can upgrade spectrum-sensing performance by exploiting the temporal features.Additionally,simulation results demonstrate that the proposed method achieves a higher probability of detection than that of the conventional energy detection(ED),the convolutional neural network(CNN),and deep neural network(DNN).Furthermore,the proposed method outperforms the CNN and the DNN in terms of a lower computational complexity.
文摘In this article, the notion of pinning control for directed networks of dynamical systems is introduced, where the nodes could be either single-input single-output （SISO） or multi-input multi-output （MIMO） dynamical systems, and could be non-identical and nonlinear in general but will be specified to be identical linear time-invariant （LTI） systems here in the study of network controllability. Both state and structural controllability problems will be discussed, illustrating how the network topology, node-system dynamics, external control inputs and inner dynamical interactions altogether affect the controllability of a general complex network of LTI systems, with necessary and sufficient conditions presented for both SISO and MIMO settings. To that end, the controllability of a special temporally switching directed network of linear time-varying （LTV） node systems will be addressed, leaving some more general networks and challenging issues to the end for research outlook.
基金Major Unified Construction Project of Petro China(2019-40210-000020-02)。
文摘Since the oil production of single well in water flooding reservoir varies greatly and is hard to predict, an oil production prediction method of single well based on temporal convolutional network(TCN) is proposed and verified. This method is started from data processing, the correspondence between water injectors and oil producers is determined according to the influence radius of the water injectors, the influence degree of a water injector on an oil producer in the month concerned is added as a model feature, and a Random Forest(RF) model is built to fill the dynamic data of water flooding. The single well history is divided into 4 stages according to its water cut, that is, low water cut, middle water cut, high water cut and extra-high water cut stages. In each stage, a TCN based prediction model is established, hyperparameters of the model are optimized by the Sparrow Search Algorithm(SSA). Finally, the models of the 4 stages are integrated into one whole-life model of the well for production prediction. The application of this method in Daqing Oilfield, NE China shows that:(1) Compared with conventional data processing methods, the data obtained by this processing method are more close to the actual production, and the data set obtained is more authentic and complete.(2) The TCN model has higher prediction accuracy than other 11 models such as Long Short Term Memory(LSTM).(3) Compared with the conventional full-life-cycle models, the model of integrated stages can significantly reduce the error of production prediction.
基金supported by the National Key R&D Program of China under Grant 2018YFB1801500.
文摘In order to reduce the physical impairment caused by signal distortion,in this paper,we investigate symbol detection with Deep Learning(DL)methods to improve bit-error performance in the optical communication system.Many DL-based methods have been applied to such systems to improve bit-error performance.Referring to the speech-to-text method of automatic speech recognition,this paper proposes a signal-to-symbol method based on DL and designs a receiver for symbol detection on single-polarized optical communications modes.To realize this detection method,we propose a non-causal temporal convolutional network-assisted receiver to detect symbols directly from the baseband signal,which specifically integrates most modules of the receiver.Meanwhile,we adopt three training approaches for different signal-to-noise ratios.We also apply a parametric rectified linear unit to enhance the noise robustness of the proposed network.According to the simulation experiments,the biterror-rate performance of the proposed method is close to or even superior to that of the conventional receiver and better than the recurrent neural network-based receiver.
文摘A lightweight multi-layer residual temporal convolutional network model(RTCN)is proposed to target the highly complex kinematics and temporal correlation of human motion.RTCN uses 1-D convolution to efficiently obtain the spatial structure information of human motion and extract the correlation in the time series of human motion.The residual structure is applied to the proposed network model to alleviate the problem of gradient disappearance in the deep network.Experiments on the Human 3.6M dataset demonstrate that the proposed method effectively reduces the errors of motion prediction compared with previous methods,especially of long-term prediction.
