QWalkVec: Node Embedding by Quantum Walk

• URL: http://arxiv.org/abs/2408.08534v1
• Date: Fri, 16 Aug 2024 05:14:38 GMT
• Title: QWalkVec: Node Embedding by Quantum Walk
• Authors: Rei Sato, Shuichiro Haruta, Kazuhiro Saito, Mori Kurokawa,
• Abstract summary: A quantum walk is a quantum version of a random walk that demonstrates a faster propagation than a random walk on a graph. We propose QWalkVec, a quantum walk-based node embedding method. We evaluate the effectiveness of QWalkVec in node classification tasks conducted on four small-sized real datasets.
• Score: 2.0749231618270803
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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