IEEE Transactions on Network Science and Engineering最新文献_第5页

Temporal Link Prediction via Auxiliary Graph Transformer 通过辅助图变换器进行时态链接预测

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-10-24 DOI: 10.1109/TNSE.2024.3485093

Tao Tan;Xianbin Cao;Fansheng Song;Shenwen Chen;Wenbo Du;Yumeng Li

{"title":"Temporal Link Prediction via Auxiliary Graph Transformer","authors":"Tao Tan;Xianbin Cao;Fansheng Song;Shenwen Chen;Wenbo Du;Yumeng Li","doi":"10.1109/TNSE.2024.3485093","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3485093","url":null,"abstract":"Temporal link prediction is fundamental for analyzing and predicting the behavior of real evolving complex systems. Recently, advances in graph learning for temporal network snapshots present a promising approach for predicting the evolving topology. However, previous methods only considered temporal-structural encoding of the entire network, which leads to the overshadowing of crucial evolutionary characteristics by massive invariant network structural information. In this paper, we delve into the evolving topology and propose an auxiliary learning framework to capture not only the overall network evolution patterns but also the time-varying regularity of the evolved edges. Specifically, we utilize a graph transformer to infer temporal networks, incorporating a temporal cross-attention mechanism to refine the dynamic graph representation. Simultaneously, a dynamic difference transformer is designed to infer the evolved edges, serving as an auxiliary task and being aggregated with graph representation to generate the final predicted result. Extensive experiments are conducted on eight real-world temporal networks from various scenarios. The results indicate that our auxiliary learning framework outperforms the baselines, demonstrating the superiority of the proposed method in extracting evolution patterns.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"11 6","pages":"5954-5968"},"PeriodicalIF":6.7,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142694647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimal Control of Nonlinear Competitive Epidemic Spreading Processes With Memory in Heterogeneous Networks 异构网络中具有记忆的非线性竞争流行病传播过程的最优控制

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-10-24 DOI: 10.1109/TNSE.2024.3486279

Jin You;Yan Li;Xiangyang Cao;Daduan Zhao;YangQuan Chen;Chenghui Zhang

{"title":"Optimal Control of Nonlinear Competitive Epidemic Spreading Processes With Memory in Heterogeneous Networks","authors":"Jin You;Yan Li;Xiangyang Cao;Daduan Zhao;YangQuan Chen;Chenghui Zhang","doi":"10.1109/TNSE.2024.3486279","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3486279","url":null,"abstract":"Modeling and control of epidemic-like spreading processes, such as contagions, rumors or products in complex networks, holds profound significance for understanding transmission mechanisms, formulating effective prevention and optimizing resource allocation. This paper investigates the optimal control problems for competing processes modeled by fractional-order bi-virus systems with memory in complex networks for the first time. The proposed model performs exceptionally well in capturing the dynamics of virus transmission and immune. First, the key indicators: bi-basic reproduction numbers (BBRNs) are introduced for the metapopulation model with nonlinear infectious functions in heterogenous networks. Then they are utilized to classify the equilibria, including extinction, absolute dominance and coexistence ones. Second, sufficient conditions for the stability of the equilibria are derived. Leveraging this result, an optimal therapeutic protocol is presented to eradicate viruses and minimize the cost function simultaneously. The corresponding optimal control solutions are generated via gradient descent algorithm, which can converge relatively quickly by iteratively adjusting control variables. Particularly, the role of fractional-order \u0000<inline-formula><tex-math>$alpha$</tex-math></inline-formula>\u0000 in system modeling and optimization is deeply probed into. Finally, numerical examples are also included at the end of this paper to further shed light on the effectiveness of our results.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 1","pages":"96-109"},"PeriodicalIF":6.7,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ULBRF: A Framework for Maximizing Influence in Dynamic Networks Based on Upper and Lower Bounds of Propagation ULBRF：基于传播上下限的动态网络影响力最大化框架

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-10-23 DOI: 10.1109/TNSE.2024.3485220

Zekun Liu;Jianyong Yu;Wei Liang;Xue Han

{"title":"ULBRF: A Framework for Maximizing Influence in Dynamic Networks Based on Upper and Lower Bounds of Propagation","authors":"Zekun Liu;Jianyong Yu;Wei Liang;Xue Han","doi":"10.1109/TNSE.2024.3485220","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3485220","url":null,"abstract":"The influence maximization problem that selects a set of seed nodes to maximize the influence spread has been becoming a hot research topic. The classical algorithms select the seed nodes at the initial moment based on the topological properties in static networks, which are not suitable for solving the problem in dynamic networks. In this paper, we propose the Upper and Lower Bound Radix Forward framework to solve this problem in the dynamic network. First, we propose the upper bound of the influence spread and derive the corresponding lower bound to find the seed nodes. When extending the classical IM to the dynamic network, we find the problem that the final influence spreads increase logarithmically with the linear expansion of the initial seed sets. Therefore, the Dynamic Network Path Dispersion is proposed to solve the problem by measuring the heterogeneity of the snaphots on the dynamic network. The Upper and Lower Bound Radix Forward (ULBRF) is tested and applied in two real dynamic networks and two synthetic networks. Experimental results show that the ULBRF is better than the widely recognized improved greedy algorithms. The Dynamic Network Path Dispersion (DNPD) and the strategy based on the heterogeneities also expand the final influence spread by \u0000<inline-formula><tex-math>$35% sim 45%$</tex-math></inline-formula>\u0000.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"11 6","pages":"6704-6717"},"PeriodicalIF":6.7,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142713760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analyzing the Semantic Structure of Network Flow: A Threat Detection Method With Independent Generalization Capabilities 网络流语义结构分析：一种具有独立泛化能力的威胁检测方法

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-10-23 DOI: 10.1109/TNSE.2024.3483216

Yiqing Luo;Mingshu He;Xiaojuan Wang

{"title":"Analyzing the Semantic Structure of Network Flow: A Threat Detection Method With Independent Generalization Capabilities","authors":"Yiqing Luo;Mingshu He;Xiaojuan Wang","doi":"10.1109/TNSE.2024.3483216","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3483216","url":null,"abstract":"Network threat detection and identification remain fundamental tasks in cyberspace defence. Existing graph-based detection methods exhibit limited capabilities in transformability and independence, necessitating a redefinition of network behaviour to enhance their applicability in scenarios such as unknown threat discovery and low sample detection. In response to these challenges, we propose a fine-grained threat detection method based on flow semantic structure, with independent generalization capabilities, to refine the definition of flow and behaviour representation in data analysis. By constructing a semantic association topology map for each flow, the proposed method utilizes behavioural data structure information to extract semantic structure features independently. Subsequently, it aggregates updated graph node information into flow-level semantic embeddings, facilitating behaviour prediction. The final evaluation results show that this method outperforms existing state-of-the-art models, achieving detection accuracies of 97.86%, 95.76%, and 99.62% on three publicly datasets, respectively. In addition, the evaluation through simulating real threat detection environments at different concentrations shows that this method can still maintain a high detection rate with a small amount of data involved in training, and has certain generalization ability for new samples.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 1","pages":"28-43"},"PeriodicalIF":6.7,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Distributed Management of Fluctuating Energy Resources in Dynamic Networked Systems 动态网络系统中波动能源的分布式管理

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-10-22 DOI: 10.1109/TNSE.2024.3484149

Xiaotong Cheng;Ioannis Tsetis;Setareh Maghsudi

{"title":"Distributed Management of Fluctuating Energy Resources in Dynamic Networked Systems","authors":"Xiaotong Cheng;Ioannis Tsetis;Setareh Maghsudi","doi":"10.1109/TNSE.2024.3484149","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3484149","url":null,"abstract":"Modern power systems integrate renewable distributed energy resources (DERs) as an environment-friendly enhancement to meet the ever-increasing demands. However, the inherent unreliability of renewable energy renders developing DER management algorithms imperative. We study the energy-sharing problem in a system consisting of several DERs. Each agent harvests and distributes renewable energy in its neighborhood to optimize the network's performance while minimizing energy waste. We model this problem as a bandit convex optimization problem with constraints that correspond to each node's limitations for energy production. We propose distributed decision-making policies to solve the formulated problem, where we utilize the notion of dynamic regret as the performance metric. We also include an adjustment strategy in our developed algorithm to reduce the constraint violations. Besides, we design a policy that deals with the non-stationary environment. Theoretical analysis shows the effectiveness of our proposed algorithm. Numerical experiments using a real-world dataset show superior performance of our proposal compared to state-of-the-art methods.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 1","pages":"54-69"},"PeriodicalIF":6.7,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Stabilization of Switched Systems With Unstable Subsystems via an Event-Triggered Control Based Codesign Method 基于事件触发控制的子系统不稳定切换系统的协同设计方法

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-10-22 DOI: 10.1109/TNSE.2024.3484488

Zhengbao Cao;Jun Fu;Ruicheng Ma

{"title":"Stabilization of Switched Systems With Unstable Subsystems via an Event-Triggered Control Based Codesign Method","authors":"Zhengbao Cao;Jun Fu;Ruicheng Ma","doi":"10.1109/TNSE.2024.3484488","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3484488","url":null,"abstract":"Two state feedback based event-triggered stabilization methods for switched systems under both synchronous phenomenon and asynchronous phenomenon are proposed, where no subsystem of the resulting closed-loop switched systems is necessarily stable. The discontinuity of triggering conditions at switching instants of controllers or subsystems can result in the samplings instantaneously, which makes obtaining the positive minimum of inter-execution times to ensure Zeno-freeness phenomenon not applicable, thus two new methods are proposed to illustrate Zeno-freeness phenomenon of two event-triggering mechanisms. Moreover, using two methods can estimate the maximum sampling number in every interval with finite length. The event-triggered control based codesign method can guarantee exponential stability and asymptotic stability with Zeno-freeness behavior of systems under synchronous phenomenon and asynchronous phenomenon respectively. Moreover, sufficient conditions on the corresponding stability are developed. Finally, two examples are presented to illustrate the effectiveness of the proposed methods.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 1","pages":"70-82"},"PeriodicalIF":6.7,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ONDS: Optimum Node and Data Selection From Constrained IoT for Efficient Online Learning 基于约束物联网的高效在线学习的最优节点和数据选择

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-10-21 DOI: 10.1109/TNSE.2024.3483295

Alaa Awad Abdellatif;Khaled Shaban;Ahmed Massoud

{"title":"ONDS: Optimum Node and Data Selection From Constrained IoT for Efficient Online Learning","authors":"Alaa Awad Abdellatif;Khaled Shaban;Ahmed Massoud","doi":"10.1109/TNSE.2024.3483295","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3483295","url":null,"abstract":"Supervised Machine Learning (ML) models require large amounts of labeled data for training. However, this becomes challenging when dealing with resource- and network-constrained Internet of Things (IoT) devices that collect data. Furthermore, in scenarios where the acquired data is fast-changing and highly temporal, continuous and online learning becomes necessary. In this paper, we address the problem of efficiently training ML models using data from IoT nodes. We specifically focus on two aspects: i) selecting the nodes that provide data for the re/training, and ii) determining the optimal amounts of data to be acquired from these nodes, considering network and time constraints, while minimizing learning errors. To tackle this optimization problem, we propose ONDS: an Optimum Node and Data Selection algorithm with linear complexity in the worst-case. ONDS offers a model-agnostic solution applicable to different data modalities and ML architectures. To evaluate the performance of ONDS, we conduct experiments using various models and real-world datasets. The results demonstrate the effectiveness of ONDS, as it outperforms existing alternatives in both classification and regression tasks.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 1","pages":"44-53"},"PeriodicalIF":6.7,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mixup in Latent Geometry for Graph Classification 图形分类中的潜在几何混合

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-10-21 DOI: 10.1109/TNSE.2024.3482188

Zijia Liu;Xiaolei Ru;Jack Murdoch Moore;Xin-Ya Zhang;Gang Yan

{"title":"Mixup in Latent Geometry for Graph Classification","authors":"Zijia Liu;Xiaolei Ru;Jack Murdoch Moore;Xin-Ya Zhang;Gang Yan","doi":"10.1109/TNSE.2024.3482188","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3482188","url":null,"abstract":"Mixup is a data augmentation method which can interpolate between existing data to create new samples. By enlarging the training distribution, it reduces the risk of over-fitting and improves generalization. Mixup is relatively straightforward to apply to image samples because pixels with equivalent coordinates in different images can be associated. However, alignment of distinct graphs with different sizes is non-trivial, thereby hindering the application of Mixup to graph data. Here we develop a novel algorithm to address this issue by exploiting the latent hyperbolic geometry which has been shown to underlie many real-world graphs. By considering global graph structure similarity and several fundamental structural features of graph models, we demonstrate that our mixup scheme leads to synthetic graphs whose structural features approximate the linear interpolation of parent graphs, a property important for avoiding the generation of mislabeled synthetic data. We apply the proposed algorithm to classify empirical graphs, and the results show that it improves classification performance on all six benchmark datasets and significantly enhances the generalization ability and robustness of graph neural networks.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"11 6","pages":"5943-5953"},"PeriodicalIF":6.7,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10723746","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142694668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Load Balancing With Traffic Splitting for QoS Enhancement in 5G HetNets 在 5G HetNets 中利用流量分流实现负载平衡以增强 QoS

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-10-21 DOI: 10.1109/TNSE.2024.3482365

Abdul Manan;Syed Maaz Shahid;SungKyung Kim;Sungoh Kwon

{"title":"Load Balancing With Traffic Splitting for QoS Enhancement in 5G HetNets","authors":"Abdul Manan;Syed Maaz Shahid;SungKyung Kim;Sungoh Kwon","doi":"10.1109/TNSE.2024.3482365","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3482365","url":null,"abstract":"In heterogeneous networks (HetNets), high user density and random small cell deployment often result in uneven User Equipment (UE) distributions among cells. This can lead to excessive resource usage in some cells and a degradation of Quality of Service (QoS) for users, even while resources in other cells remain underutilized. To address this challenge, we propose a load-balancing algorithm for 5G HetNets that employs traffic splitting for dual connectivity (DC) users. By enabling traffic splitting, DC allows UEs to receive data from both macro and small cells, thereby enhancing network performance in terms of load balancing and QoS improvement. To prevent cell overloading, we formulate the problem of minimizing load variance across 5G HetNet cells using traffic splitting. We derive a theoretical expression to determine the optimal split ratio by considering the cell load conditions. The proposed algorithm dynamically adjusts the data traffic split for DC users based on the optimal split ratio and, if necessary, offloads edge users from overloaded macro cells to underloaded macro cells to achieve uniform network load distribution. Simulation results demonstrate that the proposed algorithm achieves more even load distribution than other load balancing algorithms and increases network throughput and the number of QoS-satisfied users.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"11 6","pages":"6272-6284"},"PeriodicalIF":6.7,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142679301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Digital Twin-Empowered Task Assignment in Aerial MEC Network: A Resource Coalition Cooperation Approach With Generative Model 空中MEC网络中数字孪生授权任务分配：一种基于生成模型的资源联盟合作方法

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-10-17 DOI: 10.1109/TNSE.2024.3482327

Xin Tang;Qian Chen;Rong Yu;Xiaohuan Li

{"title":"Digital Twin-Empowered Task Assignment in Aerial MEC Network: A Resource Coalition Cooperation Approach With Generative Model","authors":"Xin Tang;Qian Chen;Rong Yu;Xiaohuan Li","doi":"10.1109/TNSE.2024.3482327","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3482327","url":null,"abstract":"To meet the demands for ubiquitous communication and temporary edge computing in 6G networks, aerial mobile edge computing (MEC) networks have been envisioned as a new paradigm. However, dynamic user requests pose challenges for task assignment strategies. Most of the existing research assumes that the strategy is deployed on ground-based stations or UAVs, which will be ineffective in an environment lacking infrastructure and continuous energy supply. Moreover, the resource mutual exclusion problem of dynamic task assignment has not been effectively solved. Toward this end, we introduce the digital twin (DT) into the aerial MEC network to study the resource coalition cooperation approach with the generative model (GM), which provides a preliminary coalition structure for the coalition game. Specifically, we propose a novel network framework that is composed of an application plane, a physical plane, and a virtual plane. After that, the task assignment problem is simplified to convex optimization programming with linear constraints. And then, we also propose a resource coalition cooperation approach that is based on a transferable utility (TU) coalition game to obtain an approximate optimal solution. Extensive analysis and numerical results confirm the effectiveness of our proposed approach in terms of energy consumption and utilization of resources.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 1","pages":"13-27"},"PeriodicalIF":6.7,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10720878","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0