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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.01918 |
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| _version_ | 1866912623118778368 |
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| author | Marın, Adrián Soto-Gomez, Mauricio Valentini, Giorgio Casiraghi, Elena Cano, Carlos Manzano, Daniel |
| author_facet | Marın, Adrián Soto-Gomez, Mauricio Valentini, Giorgio Casiraghi, Elena Cano, Carlos Manzano, Daniel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_01918 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hybrid Quantum-Classical Walks for Graph Representation Learning in Community Detection Marın, Adrián Soto-Gomez, Mauricio Valentini, Giorgio Casiraghi, Elena Cano, Carlos Manzano, Daniel Quantum Physics Computational Physics 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. |
| title | Hybrid Quantum-Classical Walks for Graph Representation Learning in Community Detection |
| topic | Quantum Physics Computational Physics |
| url | https://arxiv.org/abs/2510.01918 |