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Graphon Neural Networks and the Transferability of Graph Neural Networks
Luana Ruiz · Luiz Chamon · Alejandro Ribeiro

Thu Dec 10 09:00 AM -- 11:00 AM (PST) @ Poster Session 5 #1719

Graph neural networks (GNNs) rely on graph convolutions to extract local features from network data. These graph convolutions combine information from adjacent nodes using coefficients that are shared across all nodes. Since these coefficients are shared and do not depend on the graph, one can envision using the same coefficients to define a GNN on another graph. This motivates analyzing the transferability of GNNs across graphs. In this paper we introduce graphon NNs as limit objects of GNNs and prove a bound on the difference between the output of a GNN and its limit graphon-NN. This bound vanishes with growing number of nodes if the graph convolutional filters are bandlimited in the graph spectral domain. This result establishes a tradeoff between discriminability and transferability of GNNs.

Author Information

Luana Ruiz (University of Pennsylvania)

Luana Ruiz received the B.Sc. degree in electrical engineering from the University of São Paulo, Brazil, and the M.Sc. degree in electrical engineering from the École Supérieure d'Electricité (now CentraleSupélec), France, in 2017. She is currently a Ph.D. candidate with the Department of Electrical and Systems Engineering at the University of Pennsylvania. Her research interests are in the fields of graph signal processing and machine learning over network data. She was awarded an Eiffel Excellence scholarship from the French Ministry for Europe and Foreign Affairs between 2013 and 2015 and, in 2019, received a best student paper award at the 27th European Signal Processing Conference.

Luiz Chamon (University of Pennsylvania)
Alejandro Ribeiro (University of Pennsylvania)

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