Quantum social network analysis (QSNA) is a recent advancement in the interdisciplinary field of quantum computing and social network analysis. This manuscript comprehensively reviews QSNA, emphasizing its methodologies, implementation strategies, challenges, and potential applications. It explores the conceptual foundation of key social network analysis research problems, including link prediction, influence maximization, and community detection. The research examines how quantum algorithms can revolutionize such social network tasks by leveraging principles from quantum mechanics and information theory and highlights the advantages of quantum algorithms in handling complex social network structures. The implementation section delves into the practical aspects of QSNA, such as frameworks, experimental setups, and evaluation methods. We assess the capabilities of existing quantum programming language tools and platforms. Various case studies illustrate the potential of quantum computing to enhance the performance of social network analysis. Additionally, we identify several crucial challenges and future research directions for QSNA, including the complexity of developing quantum algorithms, the need for interdisciplinary knowledge, and the challenges of integrating quantum and classical computing resources. This paper aims to serve as a foundational resource for researchers and practitioners, providing insights into the transformative potential of quantum computing in advancing the analysis of social networks and outlining future research directions in this emerging field.

中文翻译：

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量子社交网络分析：方法、实施、挑战和未来方向


量子社交网络分析 （QSNA） 是量子计算和社交网络分析跨学科领域的最新进展。本手稿全面回顾了 QSNA，强调了其方法、实施策略、挑战和潜在应用。它探讨了关键社交网络分析研究问题的概念基础，包括链接预测、影响力最大化和社区检测。该研究考察了量子算法如何通过利用量子力学和信息论的原理来彻底改变此类社交网络任务，并强调了量子算法在处理复杂社交网络结构方面的优势。实现部分深入探讨了 QSNA 的实践方面，例如框架、实验设置和评估方法。我们评估现有量子编程语言工具和平台的功能。各种案例研究说明了量子计算在增强社交网络分析性能方面的潜力。此外，我们还确定了 QSNA 的几个关键挑战和未来的研究方向，包括开发量子算法的复杂性、对跨学科知识的需求以及集成量子和经典计算资源的挑战。本文旨在为研究人员和从业者提供基础资源，深入了解量子计算在推进社交网络分析方面的变革潜力，并概述这一新兴领域的未来研究方向。