IEEE Transactions on Network Science and Engineering最新文献_第2页

Content-Aware AP Selection With LSTM-Enabled Proactive Caching in Cell-Free Massive MIMO Networks 无小区大规模MIMO网络中支持lstm的主动缓存的内容感知AP选择

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-06-11 DOI: 10.1109/TNSE.2025.3578687

Mahnoor Ajmal;Seri Park;Malik Muhammad Saad;Muhammad Ashar Tariq;Dongkyun Kim

{"title":"Content-Aware AP Selection With LSTM-Enabled Proactive Caching in Cell-Free Massive MIMO Networks","authors":"Mahnoor Ajmal;Seri Park;Malik Muhammad Saad;Muhammad Ashar Tariq;Dongkyun Kim","doi":"10.1109/TNSE.2025.3578687","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3578687","url":null,"abstract":"Cell-Free massive MIMO (CF-mMIMO) networks face significant challenges in achieving Ultra-Reliable Low-Latency Communication (URLLC) requirements due to inherent delays in content retrieval from central processing units (CPUs). This paper presents an integrated framework that jointly optimizes access point (AP) selection and content caching to minimize latency while maintaining reliability. We develop a novel content-aware user-centric clustering scheme that considers both cached content availability and channel conditions. The scheme features a Content Query Beacon (CQB) mechanism, which verifies content availability prior to connection establishment. To address the dynamic nature of content popularity, we design a novel proactive content caching strategy using Long Short-Term Memory (LSTM) to minimize CPU-dependent data retrieval. Extensive simulations demonstrate that our proposed framework achieves a 75% reduction in content delivery latency, 31.87% improvement in Quality of Experience (QoE), and a 26.8% increase in cache hit rates compared to conventional approaches. This comprehensive solution significantly enhances the capability of CF-mMIMO networks to deliver URLLC services, particularly in densely populated areas with diverse content demands.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 6","pages":"4982-4997"},"PeriodicalIF":7.9,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145352166","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

On Random Seeding for Influence Maximization in Differentially Private Graphs: Balancing Privacy and Utility Using Percolation Theory 差分私有图中影响最大化的随机播种：利用渗透理论平衡隐私与效用

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-06-11 DOI: 10.1109/TNSE.2025.3578801

Niranjana Unnithan;Balasubramaniam Natarajan;George Amariucai

{"title":"On Random Seeding for Influence Maximization in Differentially Private Graphs: Balancing Privacy and Utility Using Percolation Theory","authors":"Niranjana Unnithan;Balasubramaniam Natarajan;George Amariucai","doi":"10.1109/TNSE.2025.3578801","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3578801","url":null,"abstract":"Influence maximization in social networks is a fundamental problem in network science with applications in viral marketing, information diffusion, and opinion formation. However, privacy concerns pose a significant challenge while designing strategies to maximize the spread of influence. In this paper, we study influence maximization under differential privacy constraints by considering two graph perturbation mechanisms: edge addition and edge addition/ deletion. We demonstrate that random seeding along with carefully crafted graph perturbation mechanisms achieve effective diffusion outcomes while preserving privacy. This approach leverages percolation theory to show that graph perturbation diminishes the value of network information, making random seeding asymptotically comparable to conventional optimization techniques in certain percolation phases. We provide theoretical proofs and experimental validations demonstrating the effectiveness of our approaches. Our methods offer a robust solution to the trade-off between privacy and utility in influence maximization, opening avenues for privacy-preserving strategies in social network analysis.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 6","pages":"4998-5011"},"PeriodicalIF":7.9,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145352161","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

Edges Matter: An Analysis of Graph Time-Series Representations for Temporal Networks 边很重要：时间网络的图时间序列表示分析

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-06-09 DOI: 10.1109/TNSE.2025.3577402

Hongjie Chen;Ryan A. Rossi;Nesreen K. Ahmed;Namyong Park;Yu Wang;Tyler Derr;Hoda Eldardiry

{"title":"Edges Matter: An Analysis of Graph Time-Series Representations for Temporal Networks","authors":"Hongjie Chen;Ryan A. Rossi;Nesreen K. Ahmed;Namyong Park;Yu Wang;Tyler Derr;Hoda Eldardiry","doi":"10.1109/TNSE.2025.3577402","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3577402","url":null,"abstract":"Representations of temporal networks arising from a stream of edges lie at the heart of models learned on it and its performance on downstream applications. Previous modeling work has mainly represented a stream of timestamped edges using a time-series of graphs based on a specific time-scale <inline-formula><tex-math>$tau$</tex-math></inline-formula> (e.g., 1 mo). In contrast, it has recently been shown that constructing a time-series of graphs where each graph maintains a fixed <inline-formula><tex-math>$epsilon$</tex-math></inline-formula> number of edges, namely <inline-formula><tex-math>$epsilon$</tex-math></inline-formula>-graph time-series, leads to better performance on downstream applications, but there has yet to be a detailed investigation on why <inline-formula><tex-math>$epsilon$</tex-math></inline-formula>-graphs outperform <inline-formula><tex-math>$tau$</tex-math></inline-formula>-graphs. In this work, we design extensive experiments on a benchmark of over 25 temporal network datasets, investigating the impact of edge randomization and the various representations on graph statistics. Our results indicate that the <inline-formula><tex-math>$epsilon$</tex-math></inline-formula>-graph time-series representation effectively captures the structural properties of the graphs across time whereas the commonly used <inline-formula><tex-math>$tau$</tex-math></inline-formula>-graph time-series mostly captures the frequency of edges. This motivates the need for a paradigm shift to developing temporal network representation learning frameworks built upon <inline-formula><tex-math>$epsilon$</tex-math></inline-formula>-graph time-series. To help pave the way, we release a benchmark for the evaluation and development of better models.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 6","pages":"4863-4875"},"PeriodicalIF":7.9,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145351910","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

Consensus-Based Event-Triggered Distributed Optimization for a Network of Agents With Cubic Objective Functions 基于共识的三次目标函数智能体网络事件触发分布式优化

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-06-09 DOI: 10.1109/TNSE.2025.3578140

Zarar Ahmed Malik;Muhammad Rehan;Waqas Ahmed;Ijaz Ahmed;Choon Ki Ahn

{"title":"Consensus-Based Event-Triggered Distributed Optimization for a Network of Agents With Cubic Objective Functions","authors":"Zarar Ahmed Malik;Muhammad Rehan;Waqas Ahmed;Ijaz Ahmed;Choon Ki Ahn","doi":"10.1109/TNSE.2025.3578140","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3578140","url":null,"abstract":"The conventional resource allocation methods, using a central node, are not resilient, owing to the failure of the central unit. An advanced solution is to apply distributed optimization by integrating intelligent nodes across a network. This paper deals with distributed optimization via the event-triggered (ET) consensus approach for nodes over a directed graph. An optimality condition for solving the optimization problem of a collective cubic objective function is provided. An optimization protocol for solving the optimization problem in a distributed manner by application of a nonlinear incremental cost (IC) consensus method is proposed. The analysis for the proposed optimization protocol has been attained by the Lyapunov function and the Lyapunov-Krasovskii functional to attain IC consensus and balance of supply-demand mismatch. In contrast to the existing works, the proposed approach (i) deals with an optimization problem for a combined cubic objective function, (ii) considers an ET mechanism for bandwitdth management, (iii) deals with a directed network topology (rather than an undirected graph), and (iv) incorporates the communication delay. Moreover, the elimination of Zeno behavior is ensured through the resultant approach. Finally, simulation experiments for the resource allocation in distributed generators of cubic objective functions are provided by considering the comparison with existing works and analysis of the presented methodology.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 6","pages":"4939-4951"},"PeriodicalIF":7.9,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145352020","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

Adaptive Edge Task Offloading via Parameterized Multi-Objective Reinforcement Learning With Hybrid Action Space 基于混合动作空间的参数化多目标强化学习自适应边缘任务卸载

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-06-09 DOI: 10.1109/TNSE.2025.3577628

Huimin Tong;Cheng Chen;Weihao Jiang;Ting Wang;Jiang Zhu

引用次数: 0

IAAE-Stega: Generic Blockchain-Based Steganography Framework via Invertible Adversarial Autoencoder IAAE-Stega：基于可逆对抗性自编码器的通用区块链隐写框架

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-06-09 DOI: 10.1109/TNSE.2025.3577778

Xiangbo Yuan;Jiahang Sun;Zhuo Chen;Chuan Zhang;Meng Li;Zijian Zhang;Liehuang Zhu

{"title":"IAAE-Stega: Generic Blockchain-Based Steganography Framework via Invertible Adversarial Autoencoder","authors":"Xiangbo Yuan;Jiahang Sun;Zhuo Chen;Chuan Zhang;Meng Li;Zijian Zhang;Liehuang Zhu","doi":"10.1109/TNSE.2025.3577778","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3577778","url":null,"abstract":"Steganography is used to transmit secret messages over public networks, which is widely used in sensitive data transmission, anti-censorship systems, etc. Traditional steganography mainly embeds information into texts, images, and videos, but it is susceptible to tampering and tracking. Blockchain has the characteristics of anonymity, non-tampering, and flooding, making the blockchain-based steganography promising for secret messaging. However, existing schemes mainly focus on the generation of message-embedded fields and overlook the impact of required extra fields on concealment. Research results show that required extra fields can greatly increase the detection rate of transactions, up to 30%. Meanwhile, the embedding rate of blockchain-based steganography is low. If information can be embedded in these fields, the transmission capability of blockchain-based steganography can be improved. Current schemes for generating these fields face challenges such as low embedding rate, low concealment, and low efficiency. We propose an invertible adversarial autoencoder (IAAE) model. Different from ordinary AAE, IAAE consists of an invertible architecture, such as 1×1 convolution and fully connected layer, to ensure the information recovery ability. Based on IAAE, we propose IAAE-Stega, which uses IAAE to generate required extra fields. IAAE-Stega is able to embed information in required extra fields and make them indistinguishable from normal fields. In IAAE-Stega, the encoder is employed to hide information and generate indistinguishable required extra fields. After receiving a set of required extra fields, the decoder is employed to extract information. Experiments show that IAAE-Stega is better than all schemes in baselines and achieves state-of-the-art performance.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 6","pages":"4906-4921"},"PeriodicalIF":7.9,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145352059","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

Frequency-Constrained Coordinated Scheduling for Asynchronous AC Systems Under Uncertainty via Distributional Robustness 基于分布鲁棒性的不确定异步交流系统频率约束协调调度

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-06-09 DOI: 10.1109/TNSE.2025.3577278

Lun Yang;Xiaoyu Cao;Yuzhou Zhou;Zhenjia Lin;Jianguo Zhou;Xiaohong Guan;Qiuwei Wu

{"title":"Frequency-Constrained Coordinated Scheduling for Asynchronous AC Systems Under Uncertainty via Distributional Robustness","authors":"Lun Yang;Xiaoyu Cao;Yuzhou Zhou;Zhenjia Lin;Jianguo Zhou;Xiaohong Guan;Qiuwei Wu","doi":"10.1109/TNSE.2025.3577278","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3577278","url":null,"abstract":"The increasing penetration of renewable energy integration in asynchronous AC systems is gradually lowering the system inertia. Concurrently, the asynchronous interconnection with high-voltage direct current (HVDC) links will limit the frequency regulation resources sharing between sending- and receiving-end grids. These issues put the frequency security of asynchronous AC systems at risk. In this context, we propose a coordinated scheduling model for the asynchronous AC systems that co-optimizes frequency regulation resources from generators, wind farms, HVDC fast-act corrections, and non-critical load shedding, and energy storage systems while guaranteeing frequency security following a contingency. The proposed model explicitly accounts for frequency constraints and manages wind power uncertainty by designing distributionally robust joint chance constraints under the Wasserstein-metric ambiguity set. We show the proposed model admits an optimization model with bi-convex constraints and then develop a sequential solution algorithm to solve it. Case studies demonstrate the effectiveness of the proposed method.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 6","pages":"4846-4862"},"PeriodicalIF":7.9,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145352220","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

Evolutionary Dynamics of Group Cooperation on Heterogeneous Higher-Order Networks 异构高阶网络中群体合作的进化动力学

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-06-09 DOI: 10.1109/TNSE.2025.3577657

Bingxin Lin;Lei Zhou;Zhi Gao;Hao Fang

{"title":"Evolutionary Dynamics of Group Cooperation on Heterogeneous Higher-Order Networks","authors":"Bingxin Lin;Lei Zhou;Zhi Gao;Hao Fang","doi":"10.1109/TNSE.2025.3577657","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3577657","url":null,"abstract":"Group cooperation is vital for the prosperity and development of human societies. Previous studies have demonstrated that network structures and their structural heterogeneities significantly affect the evolution of cooperation. Most of these studies focus on traditional networks, where edges represent pairwise interactions. However, interactions frequently go beyond pairwise connections, occurring within groups of varying sizes and exhibiting nonlinear effects. Higher-order networks capture such characteristics by allowing general group interactions among more than two individuals with hyperedges. Here, we explore the effect of degree heterogeneity and order (i.e., group size) heterogeneity on the evolution of cooperation under both linear public goods games (PGGs) and nonlinear multiplayer snowdrift games (MSGs). We find that compared with degree homogeneity, strong degree heterogeneity may inhibit the evolution of cooperation in public goods games whereas in multiplayer snowdrift games, it can instead confer additional benefits for cooperation. Moreover, our results show that order heterogeneity reduces the threshold for the evolution of cooperation in multiplayer snowdrift games while having an almost negligible impact on cooperation in public goods games. Through extensive simulations, we reveal that such differences result from the distinct payoff structures of these two games. Our work thus highlights that how structural heterogeneities of higher-order networks affect the evolution of cooperation depends on the specific games employed, and it is necessary to consider both linear and nonlinear games to uncover the intricate and unique effect of higher-order interactions on evolutionary outcomes.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 6","pages":"4894-4905"},"PeriodicalIF":7.9,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145352213","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

Straggler-Resilient Federated Learning: Tackling Computation Heterogeneity With Layer-Wise Partial Model Training in Mobile Edge Network 离散-弹性联邦学习：用分层部分模型训练解决移动边缘网络的计算异质性

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-06-06 DOI: 10.1109/TNSE.2025.3577910

Hongda Wu;Ping Wang;C V Aswartha Narayana

{"title":"Straggler-Resilient Federated Learning: Tackling Computation Heterogeneity With Layer-Wise Partial Model Training in Mobile Edge Network","authors":"Hongda Wu;Ping Wang;C V Aswartha Narayana","doi":"10.1109/TNSE.2025.3577910","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3577910","url":null,"abstract":"Federated Learning (FL) enables many resource-limited devices to train a model collaboratively without data sharing. However, many existing works focus on model-homogeneous FL, where the global and local models are the same size, ignoring the inherently heterogeneous computational capabilities of different devices and restricting resource-constrained devices from contributing to FL. In this paper, we consider model-heterogeneous FL and propose Federated Partial Model Training (<monospace>FedPMT</monospace>), where devices with smaller computational capabilities work on partial models (subsets of the global model) and contribute to the global model. Different from Dropout-based partial model generations, which remove neurons in (hidden) model layers at random, model training in <monospace>FedPMT</monospace> is achieved from the back-propagation perspective. As such, all devices in <monospace>FedPMT</monospace> prioritize the most crucial parts of the global model. Theoretical analysis shows that the proposed partial model training design has a similar convergence rate to the widely adopted Federated Averaging (FedAvg) algorithm, <inline-formula><tex-math>$mathcal {O}(1/T)$</tex-math></inline-formula>, with the sub-optimality gap enlarged by a constant factor related to the model splitting design in <monospace>FedPMT</monospace>. Empirical results show that <monospace>FedPMT</monospace> significantly outperforms the existing partial model training designs, FedDrop and HeteroFL, especially on complex tasks. Meanwhile, compared to the popular model-homogeneous benchmark, FedAvg, <monospace>FedPMT</monospace> reaches the learning target in a shorter completion time, thus achieving a better trade-off between learning accuracy and completion time.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 6","pages":"4922-4938"},"PeriodicalIF":7.9,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145352065","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

The Impact of Competitive Opinion on Epidemic Spreading and Its Applications 竞争舆论对流行病传播的影响及其应用

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-06-06 DOI: 10.1109/TNSE.2025.3577195

Qingsong Liu;Guangjie Wang

{"title":"The Impact of Competitive Opinion on Epidemic Spreading and Its Applications","authors":"Qingsong Liu;Guangjie Wang","doi":"10.1109/TNSE.2025.3577195","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3577195","url":null,"abstract":"The community's opinion on epidemics has played an important role in government departments controlling the spread of infectious diseases. However, one of the most effective ways to analyze and understand the impact of community opinions on epidemics is to establish an effective mathematical model. In this paper, we propose a nonlinear discrete-time dynamics model to investigate the impact of the competitive opinion on the epidemic spreading. For the social network with cooperative and competitive interactions, sufficient conditions guaranteeing the stability of healthy equilibrium and unhealthy equilibrium are obtained in terms of the opinion based reproduction number. By introducing the stubborn community, it is revealed that the disappearance or coexistence of the epidemic depends on the initial level of the community infection. Based on the real data from a survey conducted on a sample of U.S. residents, we employ the proposed nonlinear epidemic-opinion model to explore the impacts of the non-pharmaceutical interventions on COVID-19 in human contact network, region traveling network and Chicago transportation network, respectively. It is further validated that the non-pharmacological interventions have a significant positive impact on reducing infection.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 6","pages":"4835-4845"},"PeriodicalIF":7.9,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145352107","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