Original scientific paperLink prediction is one of the key issues of complex networks, which attracts much research interest currently.Many link prediction methods have been proposed so far.The classical random walk as an effective tool has been widely used to study the link prediction problems.Quantum walk is the quantum analogue of classical random walk.Numerous research results show that quantum algorithms using quantum walk outperform their classical counterparts in many applications, such as graph matching and searching.But there have been few studies of the link prediction based on quantum walk, especially on discrete-time quantum walk.This paper proposes a new link prediction method based on discrete-time quantum walk.Experiment results show that prediction accuracy of our method is better than the typical methods.The time complexity of our method running on classical computers, compared with the methods based on classical random walk, is slightly higher.But our method can be greatly accelerated by executing on quantum computers.
Original scientific paperLink prediction is one of the key issues of complex networks, which attracts much research interest currently.Many link prediction methods have been proposed so far.The classical random walk as an effective tool has been widely used to study the link prediction problems.Quantum walk is the quantum analogue of classical random walk.Numerous research results show that quantum algorithms using quantum walk outperform their classical counterparts in many applications, such as graph matching and searching.But there have been few studies of the link prediction based on quantum walk, especially on discrete-time quantum walk.This paper proposes a new link prediction method based on discrete-time quantum walk.Experiment results show that prediction accuracy of our method is better than the typical methods.The time complexity of our method running on classical computers, compared with the methods based on classical random walk, is slightly higher.But our method can be greatly accelerated by executing on quantum computers.
Compared with the classical random walk, the quantum walk has the advantages of less time to find the target node and less time scattered from the source node to all nodes. Therefore, in order to further improve the accuracy of link prediction algorithms, a link prediction algorithm based on discrete-time quantum walk (DTQW) on weighted graphs is proposed by integrating node attributes and introducing the idea of assigning weights to edges in network dynamics. In this algorithm, a weighted graph is first constructed based on the attribute information of nodes in the graph, and then a DTQW model is constructed based on the weighted graph. Then, under the control of the evolution operator, the walker performs a DTQW on the weighted graph; Finally, the link prediction algorithm is implemented by defining the similarity between nodes based on the average probability of the walker reaching each node. The experimental results show that in three real social networks, Douban, Weibo and Zhihu, this algorithm has different degrees of improvement in prediction accuracy and the algorithm stability is among the top in comparison with the optimal benchmark prediction algorithm.
Link prediction is a fundamental task in network science that involves estimating the likelihood of missing or future connections between nodes based on the structural patterns of a graph. While Graph Neural Networks (GNNs) have become a dominant approach, Random Walk (RW)-based methods remain highly relevant due to their lower computational demands in large-scale networks. Although recent hybrid quantum-classical walks have shown promise in exploring graph structures, existing continuous-time models are computationally expensive as they require solving high-dimensional systems of differential equations. In this paper, we propose a discrete version of a recent hybrid quantum-classical random walk algorithm that significantly improves computational efficiency by utilizing state vector representations instead of density matrices. Our method, inspired by the Quantum Jumps approach, alternates between coherent evolution and measurement-driven state collapse to generate trajectories for node embeddings. We integrate this algorithm into a link prediction pipeline and evaluate its performance across various synthetic topologies, including Grid, Power-Law, Erdös-Rényi, Barabási-Albert, and Community graphs. The experimental results demonstrate that the proposed discrete hybrid walks consistently outperform classical random walk approaches in Community and Grid topologies, showing that quantum-inspired dynamics can capture complex structural information more effectively than classical dynamics for specific topologies.
Predicting new links in physical, biological, social, or technological networks has a significant scientific and societal impact. Path-based link prediction methods utilize the explicit counting of even- and odd-length paths between nodes to quantify a score function and infer new or unobserved links. Here, we propose a quantum algorithm for path-based link prediction using a controlled continuous-time quantum walk to encode even and odd path-based prediction scores. Through classical simulations on a few real networks, we confirm that the quantum walk scoring function performs similarly to other path-based link predictors. In a brief complexity analysis we identify the potential of our approach in uncovering a quantum speedup for path-based link prediction.
Quantum walks, the quantum mechanical counterpart of classical random walks, is an advanced tool for building quantum algorithms that has been recently shown to constitute a universal model of quantum computation. Quantum walks is now a solid field of research of quantum computation full of exciting open problems for physicists, computer scientists and engineers. In this paper we review theoretical advances on the foundations of both discrete- and continuous-time quantum walks, together with the role that randomness plays in quantum walks, the connections between the mathematical models of coined discrete quantum walks and continuous quantum walks, the quantumness of quantum walks, a summary of papers published on discrete quantum walks and entangle-ment as well as a succinct review of experimental proposals and realizations of discrete-time quantum walks. Furthermore, we have reviewed several algorithms based on both discrete- and continuous-time quantum walks as well as a most important result: the computational universality of both continuous- and discrete-time quantum walks.
A random walk is known as a random process which describes a path including a succession of random steps in the mathematical space. It has increasingly been popular in various disciplines such as mathematics and computer science. Furthermore, in quantum mechanics, quantum walks can be regarded as quantum analogues of classical random walks. Classical random walks and quantum walks can be used to calculate the proximity between nodes and extract the topology in the network. Various random walk related models can be applied in different fields, which is of great significance to downstream tasks such as link prediction, recommendation, computer vision, semi-supervised learning, and network embedding. In this article, we aim to provide a comprehensive review of classical random walks and quantum walks. We first review the knowledge of classical random walks and quantum walks, including basic concepts and some typical algorithms. We also compare the algorithms based on quantum walks and classical random walks from the perspective of time complexity. Then we introduce their applications in the field of computer science. Finally we discuss the open issues from the perspectives of efficiency, main-memory volume, and computing time of existing algorithms. This study aims to contribute to this growing area of research by exploring random walks and quantum walks together.
Predicting new links in physical, biological, social, or technological networks has a significant scientific and societal impact. Path-based link prediction methods utilize explicit counting of even and odd-length paths between nodes to quantify a score function and infer new or unobserved links. Here, we propose a quantum algorithm for path-based link prediction, QLP, using a controlled continuous-time quantum walk to encode even and odd path-based prediction scores. Through classical simulations on a few real networks, we confirm that the quantum walk scoring function performs similarly to other path-based link predictors. In a brief complexity analysis we identify the potential of our approach in uncovering a quantum speedup for path-based link prediction.