5 citations · 9 across the 6 of their papers we have counts for
3 papers · 1 filter
k-hop Graph Neural Networks
Giannis Nikolentzos, George Dasoulas, Michalis Vazirgiannis
Graph neural networks (GNNs) have emerged recently as a powerful architecture for learning node and graph representations. Standard GNNs have the same expressive power as the Weisf…
Message Passing Graph Kernels
Giannis Nikolentzos, Michalis Vazirgiannis
Graph kernels have recently emerged as a promising approach for tackling the graph similarity and learning tasks at the same time. In this paper, we propose a general framework for…
GraKeL: A Graph Kernel Library in Python
Giannis Siglidis, Giannis Nikolentzos, Stratis Limnios +3
The problem of accurately measuring the similarity between graphs is at the core of many applications in a variety of disciplines. Graph kernels have recently emerged as a promisin…