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 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.

Journal of Applied Automation TechnologiesSep 26, 20250 citations

Quantum social network analysis: Methodology, implementation, challenges, and future directions

Shashank Sheshar Singh, Sumit Kumar, Sunil Kumar Meena +3 more

Information FusionNov 26, 202425 citations

Quantum Algorithmic Frameworks for Graph-Based Optimization and Network Analysis in Large-Scale Computational Systems

Beyza Altan

The increasing scale and interconnectedness of modern computational systems have intensified the demand for efficient algorithms capable of analyzing complex networks and solving graph-based optimization problems. Classical optimization techniques, while effective for moderate-sized datasets, often encounter exponential computational growth when applied to large-scale graph structures characterized by high dimensionality and dynamic topology. This paper explores quantum algorithmic frameworks for graph-based optimization and network analysis in large-scale computational systems, emphasizing the potential of quantum computing paradigms to address combinatorial complexity and enhance computational scalability. By synthesizing developments in quantum search algorithms, variational quantum circuits, and hybrid quantum–classical optimization methods, the study evaluates their applicability to tasks such as shortest-path discovery, clustering, community detection, and network resilience assessment. The findings suggest that quantum-enabled frameworks may provide complementary advantages in exploring vast solution spaces and improving optimization efficiency, particularly when integrated with classical preprocessing and orchestration strategies.The increasing scale and interconnectedness of modern computational systems have intensified the demand for efficient algorithms capable of analyzing complex networks and solving graph-based optimization problems. Classical optimization techniques, while effective for moderate-sized datasets, often encounter exponential computational growth when applied to large-scale graph structures characterized by high dimensionality and dynamic topology. This paper explores quantum algorithmic frameworks for graph-based optimization and network analysis in large-scale computational systems, emphasizing the potential of quantum computing paradigms to address combinatorial complexity and enhance computational scalability. By synthesizing developments in quantum search algorithms, variational quantum circuits, and hybrid quantum–classical optimization methods, the study evaluates their applicability to tasks such as shortest-path discovery, clustering, community detection, and network resilience assessment. The findings suggest that quantum-enabled frameworks may provide complementary advantages in exploring vast solution spaces and improving optimization efficiency, particularly when integrated with classical preprocessing and orchestration strategies.

Next generation.Feb 20, 20260 citations

Community detection in network using Szegedy quantum walk

Md Samsur Rahaman, Supriyo Dutta

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, $l$ -partition graph, Karate club graph, and the dolphin's social network, among others.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, $l$-partition graph, Karate club graph, and the dolphin's social network, among others.

quant-pharXiv:2601.21152Jan 30, 20260 citations

Quantum Computing for Graph Optimization: An Approach to the Shortest Path Problem

Aniket Bembale, Nagendra Singh, P. Vinayaka +1 more

Lecture notes in networks and systemsDec 1, 20250 citations

Quantum Graphs and Optimization

Sovan Samanta

Studies in computational intelligenceJan 1, 20250 citations

l

-partition graph, Karate club graph, and the dolphin's social network, among others.