3 papers
quant-ph2025
Hybrid Quantum-Classical Walks for Graph Representation Learning in Community Detection
Adrián Marın, Mauricio Soto-Gomez, Giorgio Valentini +3
Graph Representation Learning (GRL) has emerged as a cornerstone technique for analysing complex, networked data across diverse domains, including biological systems, social networ…
cs.LG2021
GRAPE for Fast and Scalable Graph Processing and random walk-based Embedding
Luca Cappelletti, Tommaso Fontana, Elena Casiraghi +8
Graph Representation Learning (GRL) methods opened new avenues for addressing complex, real-world problems represented by graphs. However, many graphs used in these applications co…
cs.LG2021
Het-node2vec: second order random walk sampling for heterogeneous multigraphs embedding
Mauricio Soto-Gomez, Peter Robinson, Carlos Cano +6
Many real-world problems are naturally modeled as heterogeneous graphs, where nodes and edges represent multiple types of entities and relations. Existing learning models for heter…