标题： 基于混合量子-经典漫步的社区检测图表示学习 显示英文标题

标题： Hybrid Quantum-Classical Walks for Graph Representation Learning in Community Detection

摘要： 图表示学习（GRL）已成为分析跨不同领域复杂网络数据的核心技术，包括生物系统、社交网络和数据分析。 传统GRL方法在捕捉复杂图中的复杂关系时常常遇到困难，特别是那些表现出非平凡结构特性的图，如幂律分布或分层结构。 本文介绍了一种新颖的受量子启发的GRL算法，利用混合量子-经典行走来克服这些限制。 我们的方法结合了量子和经典动力学的优点，使行走者能够同时探索图中的高度局部和远距离连接。 针对网络社区检测的一个案例研究的初步结果表明，这种混合动力学使算法能够有效地适应复杂的图结构，为GRL任务提供了一个强大且多功能的解决方案。 显示英文摘要

摘要： Graph Representation Learning (GRL) has emerged as a cornerstone technique for analysing complex, networked data across diverse domains, including biological systems, social networks, and data analysis. Traditional GRL methods often struggle to capture intricate relationships within complex graphs, particularly those exhibiting non-trivial structural properties such as power-law distributions or hierarchical structures. This paper introduces a novel quantum-inspired algorithm for GRL, utilizing hybrid Quantum-Classical Walks to overcome these limitations. Our approach combines the benefits of both quantum and classical dynamics, allowing the walker to simultaneously explore both highly local and far-reaching connections within the graph. Preliminary results for a case study in network community detection shows that this hybrid dynamic enables the algorithm to adapt effectively to complex graph topologies, offering a robust and versatile solution for GRL tasks.