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Hybrid Quantum-Classical Walks for Graph Representation Learning in Community Detection
Adri\'an Mar{\i}n, Mauricio Soto-Gomez, Giorgio Valentini, Elena Casiraghi, Carlos Cano, Daniel Manzano
https://arxiv.org/abs/2510.01918

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

Fair Minimum Labeling: Efficient Temporal Network Activations for Reachability and Equity
Lutz Oettershagen, Othon Michail
https://arxiv.org/abs/2510.03899 https://

Fair Minimum Labeling: Efficient Temporal Network Activations for Reachability and Equity
Balancing resource efficiency and fairness is critical in networked systems that support modern learning applications. We introduce the Fair Minimum Labeling (FML) problem: the task of designing a minimum-cost temporal edge activation plan that ensures each group of nodes in a network has sufficient access to a designated target set, according to specified coverage requirements. FML captures key trade-offs in systems where edge activations incur resource costs and equitable access is essential,…