2 papers
cs.LG2024
Deep-Graph-Sprints: Accelerated Representation Learning in Continuous-Time Dynamic Graphs
Ahmad Naser Eddin, Jacopo Bono, David Aparício +3
Continuous-time dynamic graphs (CTDGs) are essential for modeling interconnected, evolving systems. Traditional methods for extracting knowledge from these graphs often depend on f…
cs.LG2023
From random-walks to graph-sprints: a low-latency node embedding framework on continuous-time dynamic graphs
Ahmad Naser Eddin, Jacopo Bono, David Aparício +4
Many real-world datasets have an underlying dynamic graph structure, where entities and their interactions evolve over time. Machine learning models should consider these dynamics…