46 citations · 49 across the 4 of their papers we have counts for
3 papers · 1 filter
Studying and Improving Graph Neural Network-based Motif Estimation
Pedro C. Vieira, Miguel E. P. Silva, Pedro Manuel Pinto Ribeiro
Graph Neural Networks (GNNs) are a predominant method for graph representation learning. However, beyond subgraph frequency estimation, their application to network motif significa…
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…
GoT-WAVE: Temporal network alignment using graphlet-orbit transitions
David Aparício, Pedro Ribeiro, Tijana Milenković +1
Global pairwise network alignment (GPNA) aims to find a one-to-one node mapping between two networks that identifies conserved network regions. GPNA algorithms optimize node conser…