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IEEE Transactions on Computational Social Systems

Quantum-Social Network Analysis for Community Detection: A Comprehensive Review

Samya Muhuri, Shashank Sheshar Singh

Published May 30, 2024• 14 citations
Publisher

Abstract

The dynamics and underlying structure of complex social networks (SNs) can be discovered using community detection. Considering how quantum computing might improve community detection techniques is becoming more and more popular in light of recent developments in computing technology. In this article, the rapidly developing topic of quantum-SN analysis for community discovery is thoroughly reviewed. Community detection is studied in the context of several quantum-inspired techniques, including quantum annealing and quantum-inspired optimization. To make use of the strengths of both conventional SN research methods and quantum computing techniques, hybrid quantum-classical approaches are being investigated. Case studies and applications that have made use of quantum-SN analysis methodologies are reviewed to highlight the practical consequences and potential advantages over conventional methods. The article also highlights the challenges and limitations of using quantum computing for SN analysis, including technical constraints and ethical issues. Finally, prospects and future research objectives in the area of quantum-SN analysis are highlighted. This covers possible developments in quantum algorithms for community detection, the incorporation of quantum computing with other SN research tasks, and the significance of multidisciplinary collaborations.

DOI
10.1109/tcss.2024.3397967

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Rising quantum technologies undertake to provide new strategies for solving hard combinatorial problems. Community detection is a fundamental issue in network studies with applications in various complex systems. A review of methods proposed in the last years in the context of quantum optimization and quantum-inspired metaheuristics to detect communities in networks is presented. Quantum optimization is introduced by recalling the main available computation models and the most popular hybridization approaches. The advantages and limitations of methods based on quantum computing and evolutionary computing are discussed.
Social network analysis (SNA) has been the topic of interest for researchers and analysts for quite a while as social networks have become the indispensable part of the semantic web. SNA has now forked itself into community detection to cater to the needs of business analytics, health monitoring, military, academia, recommendation systems, etc.; thus utilizing and processing huge amount of data. In this regard, quantum computing (QC) has has come in time to augment the classical systems with quantum characteristics ensuring unprecedented data storage and manipulation capabilities. It thus becomes necessary to facilitate effective data utility in SNA by incorporating emerging technologies like QML (Quantum machine learning) in the domain of community detection. The paper aims to present a brief insight into social network analysis and community detection highlighting the limitations prevalent in the current technologies. A systematic analysis of evolution of QML from QC has been presented whilst reviewing and discussing the relevance of QML based community detection algorithms in resolving the issues associated with the current approaches. Lastly, QML has been proposed as a novel approach to harness the power of ML (Machine learning) in quantum empowered systems by illustrating its supremacy to classical ML based community detection algorithms.
In this research, the computational efficiency of large-scale social network analysis is enhanced by the use of quantum random walk algorithms. The goal of this project is to address the need for high-efficiency centralized analysis of large-scale, heterogeneous social networks, community discovery, and dynamic effect mapping. In a theoretically sound framework of quantum random walks, amplitude superposition and unitary evolution principles have been used to improve both the sensitivity to local and global aspects. The solution uses real social network data with up to 5,000 nodes and more than 60,000 edges, and the testing results demonstrate its excellent scalability and fast convergence. Quantum algorithms have discovered community divisions and cut the mixing time by roughly 2.3 times after multiple optimizations. The performance indicators demonstrate that the above method has greatly improved key node detection accuracy and computation time as compared to the conventional baseline model in noisy situations. According to the aforementioned research, quantum-inspired algorithms have been used to expedite the analysis of large-scale data and reveal hidden structures that are challenging to identify using conventional techniques, such as bridge nodes and core-periphery distributions. The research assist data-driven choices in digital social ecosystems and lay the groundwork for in-depth network analysis.
In a network, the vertices with similar characteristics construct communities. The vertices in a community are well-connected. Detecting the communities in a network is a challenging and important problem in the theory of complex networks. One approach to solving this problem uses the classical random walks on graphs. In quantum computing, quantum walks are the quantum mechanical counterparts of classical random walks. In this article, we employ a variant of Szegedy's quantum walk to develop a procedure for discovering the communities in networks. The limiting probability distribution of quantum walks assists us in determining the inclusion of a vertex in a community. We apply our community detection procedure to a variety of graphs and social networks, including the relaxed caveman graph, lll-partition graph, Karate club graph, and the dolphin's social network, among others.
Quantum computing introduces a revolutionary computational paradigm for social data analysis. Traditional social data processing methods face significant challenges in handling high-dimensional, heterogeneous, and dynamic datasets. Quantum computing, leveraging quantum superposition, entanglement, and parallel computation, provides efficient solutions for large-scale optimization, pattern recognition, and decision analysis. This paper systematically explores the applications of quantum computing in social sciences, covering theoretical foundations, methodological frameworks, experimental validations, and case studies to highlight the advantages of quantum algorithms in social network analysis, sentiment classification, and economic forecasting. Additionally, we discuss the broader implications of quantum computing for social science research paradigms, ethical and governance concerns, and future development directions, emphasizing the integration of quantum computing with artificial intelligence. This paper aims to provide new insights into computational social science and advance the practical adoption of quantum computing in social data processing.
Abstract Currently, E-commerce is widely adopted as it is important for business management and economic growth in the new global economy, and to reach the rapid increasing population. To better manage the e-commerce, it is important to collect and evaluate the Consumer Behaviour data for decision making and optimization. The conventional computing technologies need high amount of power and time for large data analysis. Quantum computing has the potential to analyze the large amount of data more efficiently than classical computing. This paper aims to explore the core process areas that need to be consider by the practitioners for adopting quantum computing in social business. To address the objective of this study, we conducted a literature review and empirical study to explore the core process areas that need to be considered for the consideration of quantum computing in social business. The results show that limited scalability, regulatory challenges, high initial cost, limited availability of quantum resources, education and training and security are the most important process areas that need to be focused by the industry practitioners. The result of this study gives a knowledge base for research and practitioners community to develop the tools and strategies for the adoption of quantum computing in social business.