40 citations · 73 across the 12 of their papers we have counts for
4 papers · 1 filter
DINE: Dimensional Interpretability of Node Embeddings
Simone Piaggesi, Megha Khosla, André Panisson +1
Graphs are ubiquitous due to their flexibility in representing social and technological systems as networks of interacting elements. Graph representation learning methods, such as…
Evaluating Link Prediction Explanations for Graph Neural Networks
Claudio Borile, Alan Perotti, André Panisson
Graph Machine Learning (GML) has numerous applications, such as node/graph classification and link prediction, in real-world domains. Providing human-understandable explanations fo…
Beyond One-Hot-Encoding: Injecting Semantics to Drive Image Classifiers
Alan Perotti, Simone Bertolotto, Eliana Pastor +1
Images are loaded with semantic information that pertains to real-world ontologies: dog breeds share mammalian similarities, food pictures are often depicted in domestic environmen…
Fast and Effective GNN Training through Sequences of Random Path Graphs
Francesco Bonchi, Claudio Gentile, Francesco Paolo Nerini +2
We present GERN, a novel scalable framework for training GNNs in node classification tasks, based on effective resistance, a standard tool in spectral graph theory. Our method prog…