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arXiv · 2408.08534

QWalkVec: Node Embedding by Quantum Walk

Rei Sato·Shuichiro Haruta·Kazuhiro Saito·Mori Kurokawa
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Abstract

In this paper, we propose QWalkVec, a quantum walk-based node embedding method. A quantum walk is a quantum version of a random walk that demonstrates a faster propagation than a random walk on a graph. We focus on the fact that the effect of the depth-first search process is dominant when a quantum walk with a superposition state is applied to graphs. Simply using a quantum walk with its superposition state leads to insufficient performance since balancing the depth-first and breadth-first search processes is essential in node classification tasks. To overcome this disadvantage, we formulate novel coin operators that determine the movement of a quantum walker to its neighboring nodes. They enable QWalkVec to integrate the depth-first search and breadth-first search processes by prioritizing node sampling. We evaluate the effectiveness of QWalkVec in node classification tasks conducted on four small-sized real datasets. As a result, we demonstrate that the performance of QWalkVec is superior to that of the existing methods on several datasets. Our code will be available at \url{https://github.com/ReiSato18/QWalkVec}.

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Rei Sato, Shuichiro Haruta, Kazuhiro Saito, Mori Kurokawa. 2024-08-16. QWalkVec: Node Embedding by Quantum Walk. https://doi.org/10.1007/978-981-97-2242-6_8

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