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cs.LG2023
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…
cs.LG2023★ 1 cited
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…