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Research on Big Data Distributed computing Model and Algorithm Optimization for Complex network Analysis

Research on Big Data Distributed Computing Model and Algorit...

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Digital Analysis and Processing, Intelligent Computation (DAPIC), international conference on

作者： Aiting Sun Liaoning Vocational College of light industry Liaoning China

ISBN: (数字)9798331536169

ISBN: (纸本)9798331536176

With the advent of the era of big data, the traditional stand-alone computing model can no longer meet the needs of complex network analysis, and the distributed computing model provides a new solution. This paper first introduces the importance of complex network analysis and the challenges brought by big data, then discusses the application of distributed computing model in complex network analysis in detail, and focuses on the distributed computing algorithm based on MapReduce. Then, this paper puts forward an optimization strategy of distributed community detection algorithm based on modularity optimization. This strategy performs community detection tasks in parallel through MapReduce framework, and optimizes community division through iterative updating to maximize modularity. The experimental results show that the optimized algorithm has a significant improvement in computing time, accuracy and resource consumption. The distributed computing model based on MapReduce framework and its optimization strategy have obvious advantages in dealing with large-scale complex networks. It can not only improve the calculation efficiency and accuracy, but also reduce the resource consumption, which provides an efficient and feasible solution for complex network analysis.

关键词： Analytical models Accuracy Computational modeling Memory management Complex networks Big Data Data models Distributed computing Detection algorithms Optimization

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Problem Specific Communications in Distributed Quantum computing

Problem Specific Communications in Distributed Quantum Compu...

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Quantum Communications, networking, and computing (QCNC), international conference on

作者： Joyanta Basak Bing Wang Sanguthevar Rajasekaran School of Computing University of Connecticut USA

ISBN: (数字)9798331531591

ISBN: (纸本)9798331531607

When all the qubits needed for solving a problem are not located in a single quantum computer, qubits from different quantum computers can be collectively utilized. In this case, quantum communication is needed for the multiple quantum computers to communicate with each other. Several studies address the problem of minimizing the number of quantum communications when evaluating a general quantum circuit. The solutions proposed typically involve solving some intractable problems. In this paper, we show that we can obtain much better solutions when we focus on solving specific problems (instead of seeking solutions for generic circuits). Specifically, we consider several fundamental quantum circuits and identify communication protocols that need a much smaller number of communication steps than those offered by generic solutions. Our work is in line with traditional parallel and distributed computing research where typically scientists focus on solving specific problems (such as sorting, matrix multiplication, network flow, etc.) in a parallel or distributed setting.

关键词： Quantum computing Protocols Source coding Qubit Search problems Complexity theory Quantum networks Quantum circuit parallel algorithms Sorting

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Dynamic Privacy Protection with Large Language Model in Social networks 24th

Dynamic Privacy Protection with Large Language Model in So...

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24th international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2024

作者： Xie, Yizhe Zhu, Congcong Zhang, Xinyue Hu, Xiangyu Liu, Xuan School of Data Science City University of Macau Macau China Hainan International College Minzu University of China Hainan China School of Information Engineering Minzu University of China Beijing China School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan China

ISBN: (纸本)9789819615445

In contemporary social networks, dynamic privacy protection remains a pivotal yet challenging endeavor due to the intricate and evolving nature of information exchange. Traditional privacy models, predominantly static, falter in effectively safeguarding privacy amidst the complex interplay of continuously changing network interactions and structures. Addressing these deficiencies, this study introduces a novel dynamic privacy protection system anchored by large language model (LLM). Leveraging the natural language processing prowess of LLM, this system excels in real-time, context-sensitive analysis and protection of textual data within vast and variable social networks. By integrating closed-loop control theory, the system adeptly balances robust privacy safeguards with the requisite fluidity of information exchange. Experimental validations on large network datasets illustrate the system’s adeptness in balancing privacy leaks and information distortion through intelligent adaptations to privacy thresholds and strategic noise injection. The outcomes highlight the system’s utility in enhancing data security and operational efficiency, promising significant implications for future applications in broader domains such as mobile computing and IoT. This study not only propels forward the capabilities of dynamic privacy protection mechanisms but also sets a foundational architecture for subsequent innovations in privacy-sensitive, data-intensive environments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Differential privacy

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Optimizing Transitive Closure Computation for High Performance computing and Security

Optimizing Transitive Closure Computation for High Performan...

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Euromicro conference on parallel, Distributed and network-Based Processing

作者： Elio Masciari Enea Vincenzo Napolitano DIETI University of Naples Federico II Naples Italy

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Transitive closure computation is a fundamental operation in graph theory with applications in various domains. However, the increasing size and complexity of real-world graphs make traditional algorithms inefficient, especially when dealing with large datasets. This paper investigates the optimisation of transitive closure algorithms for high performance computing (HPC) applications. We implement and compare three different methods for computing the adjacency matrix of the transitive closure, based on three different Python libraries (networkX, PyTorch and NumPy). Our approach is benchmarked on seven real-world datasets of varying size and density to evaluate performance and scalability. The results show that NumPy achieves the best performance for large and dense graphs. The paper concludes with a discussion of the potential benefits of algorithmic optimization in HPC and security.

关键词： Heuristic algorithms High performance computing Scalability Memory management Data structures Real-time systems Computational efficiency Security Sparse matrices Optimization

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Grover’s Algorithm for Neural networks

Grover’s Algorithm for Neural Networks

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Quantum Communications, networking, and computing (QCNC), international conference on

作者： Andrew Haverly Shahram Rahimi Mark A. Novotny Department of Computer Science & Engineering Mississippi State University Department of Computer Science The University of Alabama Department of Physics and Astronomy Mississippi State University

ISBN: (数字)9798331531591

ISBN: (纸本)9798331531607

This paper introduces an application of Grover’s algorithm to optimize neural network training by eliminating the computationally demanding backward propagation. It clarifies previous assertions regarding training neural networks with Grover’s algorithm. The paper introduces parallel data training and training on exponentially seeded datasets. A quantum binary neural network is created to more efficiently implement neural networks on quantum computers. This trained discriminatory quantum binary neural network is shown to behave as a generative quantum binary neural network. We present methods that both simplify and enhance the process of neural network development and training through Grover’s algorithm. The quantum binary neural network has the potential to far outpace classical neural network training in terms of training time and energy.

关键词： Training Computers Backpropagation Quantum computing Machine learning algorithms Neural networks Machine learning Computer architecture Quantum communication

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SPARE: Self-adaptive Platform for Allocating Resources in Emergencies for Urgent Edge computing

SPARE: Self-adaptive Platform for Allocating Resources in Em...

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Euromicro conference on parallel, Distributed and network-Based Processing

作者： Valerio Besozzi Marco Danelutto Patrizio Dazzi Emanuele Carlini Matteo Mordacchini Department of Computer Science University of Pisa Pisa Italy National Research Council Pisa Italy

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

This paper presents SPARE, a novel serverless platform that supports self-adaptive resource allocation and reconfiguration, thereby increasing the availability of computing resources for time-critical tasks in urgent events. In emergency scenarios, SPARE reallocates resources by forwarding serverless function invocations to the nearest edge nodes having sufficient capacity. Additionally, the platform employs the use of unikernels and lightweight virtualization through Firecracker, which helps to reduce cold start times and improve function responsiveness. The experimental results demonstrate that SPARE is capable of releasing up to one-third of edge nodes within a serverless edge platform, while only experiencing a mild increase in latency, thus maintaining service continuity.

关键词： Performance evaluation Serverless computing Computer architecture Time factors Virtualization Edge computing Resilience

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BDFC:A New Flow Control Mechanism for Torus networks 24th

BDFC:A New Flow Control Mechanism for Torus Networks

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24th international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2024

作者： Zhang, Haofei Li, Youmeng Deng, Yu Fang, Qi College of Intelligence and Computing Tianjin University Tianjin300072 China Phytium Technology Co. Ltd. Changsha410073 China

ISBN: (纸本)9789819615506

FBFC (Flit Bubble Flow Control) and Dateline flow control are two commonly used flow control mechanisms in Torus networks. However, each of these two flow control mechanisms has its own performance limitations. The bubble flow control mechanism can lead to starvation, and dateline flow control can decrease channel cache utilization. We propose BDFC (Bubble Dateline Flow Control) technology, which combines the above two flow control mechanisms. It greatly alleviates the hunger problem of bubble flow control technology without increasing costs, while also increasing the cache utilization of Dateline technology, thereby improving the performance of the entire network. In 8×8Torus, its saturated throughput has increased by a maximum of 44% compared to FBFC. Improved by a maximum of 68% compared to Dateline. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Fiber optic networks

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Neural network Model for Enhancing the Crop Productivity and Effective Fertilizers 15th

Neural Network Model for Enhancing the Crop Productivity and...

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15th international conference on Soft computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired computing, NaBIC 2023

作者： Raju, K. Srujan Kotha, Mahesh Pandey, Lal Bahadur Sravanthi, G.S. Sravanthi, P. Sivaiah, Borra CMR Technical Campus Telangana Hyderabad India CMR Engineering College Telangana Hyderabad India CMR College of Engineering and Technology Telangana Hyderabad India

ISBN: (纸本)9783031810855

This paper explores the use of artificial neural networks to reduce fertilizer consumption and increase agricultural output. The two components of the system are the expert knowledge based on the land report that stimulates the potential yield through appropriate organic matter deficient in minerals in the soil. There are eight parallel systems in the system structure. Expert farmer interviews along with professionals in the fields of water, soil, and agronomy formed the basis of the integrated knowledge and formation. Over the course of three years, an extensive daily field measurement program and laboratory analysis were used to assess fertilizer consumption in the agroclimatic zone and calculate the precise amount of fertilizer required for each individual farm. In order to determine whether an artificial neural network may be feasible and produce the desired results for the crops, the aforementioned data was examined using MATLAB. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Artificial Neural network Crop Productivity Fertilizers

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A Gaussian process method with a neural network for compressible shock capturing 16

A Gaussian process method with a neural network for compress...

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16th international conference on Numerical Modeling of Space Plasma Flows, ASTRONUM 2024

作者： Lee, Dongwook Waterhouse, Henry DeGrendele, Christopher Department of Applied Mathematics University of California Santa CruzCA United States

We present a machine learning-based shock-capturing approach called the GP-NN method. The new GP-NN method is similar to the existing a posteriori GP-MOOD method in the sense of combining high- and low-order solutions away from and near shocks, respectively, to balance the accuracy and stability of the solution. However, GP-NN is different from GP-MOOD in that GP-NN is an a priori method from the shock-capturing perspective. In GP-NN, we delve into integrating machine learning within the GP-MOOD framework to replace the a posteriori MOOD shock detection mechanism with a new a priori neural network (NN) shock detector. Using NN to distinguish shock cells from non-shock cells, our a priori GP-NN approach achieves an unparalleled equilibrium between high-order accuracy and stability, dynamically adjusting to local flow conditions. Most importantly, the NN-based a priori shock-capturing approach allows to mitigate two major drawbacks in the a posteriori MOOD shock detection mechanism such as workload imbalance in parallel computing and computational inefficiency in implicit solves. This sophisticated strategy optimizes numerical accuracy and stability while maintaining the overall predictive capabilities to simulate shock-dominant flows. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Engineering education

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Automatic Modulation Recognition Using parallel Feature Extraction Architecture 18th

Automatic Modulation Recognition Using Parallel Feature Extr...

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18th international conference on Wireless Artificial Intelligent computing Systems and Applications, WASA 2024

作者： Tang, Haolin Zhao, Yanxiao Kuzlu, Murat Luo, Changqing Catak, Ferhat Ozgur Wang, Wei Department of Electrical and Computer Engineering Virginia Commonwealth University RichmondVA United States Department of Engineering Technology Old Dominion University NorfolkVA United States Department of Computer Science Virginia Commonwealth University RichmondVA United States Department of Electrical Engineering and Computer Science University of Stavanger Rogaland Norway Department of Computer Science San Diego State University San DiegoCA United States

ISBN: (纸本)9783031714665

Automatic Modulation Recognition (AMR) plays a critical role in wireless communication and can be applied in various applications such as spectrum monitoring and signal surveillance. Recently, different AMR approaches have been proposed based on Deep Learning (DL), which are inspired by the powerful abilities of feature extraction in neural networks, i.e., Convolutional Neural networks (CNN) and Recurrent Neural networks (RNN). Among these solutions, hybrid methods extract spatial features and temporal features of signals by combining CNN and RNN into a single architecture to provide better performance. However, it is inefficient to extract two types of features using a single architecture since the temporal feature could be degraded after CNN layers, and the spatial feature may be compromised after RNN layers. To overcome this challenge, we propose a parallel neural network architecture for AMR, which extracts spatial features using CNN layers and temporal features using LSTM (Long Short-Term Memory) layers, respectively, in two parallel routes. Afterward, the extracted features will be combined to predict the modulation scheme of a signal. Extensive simulations are conducted on signals with 11 different modulation methods at a wide range of Signal-to-Noise Ratio (SNR) levels to evaluate our proposed parallel feature extraction architecture. In addition, we compare our solution against three other DL-based methods, and the results show that our method outperforms in terms of recognition accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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