On the Trade-off between Over-smoothing and Over-squashing in Deep Graph Neural Networks

Author:

Giraldo Jhony H.1ORCID,Skianis Konstantinos2ORCID,Bouwmans Thierry3ORCID,Malliaros Fragkiskos D.4ORCID

Affiliation:

1. LTCI, Télécom Paris - Institut Polytechnique de Paris, Palaiseau, France

2. BLUAI, Athens, Greece

3. Laboratoire MIA, La Rochelle Université, La Rochelle, France

4. Université Paris-Saclay, CentraleSupélec, Inria, Gif-sur-Yvette, France

Funder

DATAIA

Agence Nationale de la Recherche

Publisher

ACM

Reference59 articles.

1. Uri Alon and Eran Yahav . 2021 . On the bottleneck of graph neural networks and its practical implications . In International Conference on Learning Representations. Uri Alon and Eran Yahav. 2021. On the bottleneck of graph neural networks and its practical implications. In International Conference on Learning Representations.

2. Adrien Benamira , Benjamin Devillers , Etienne Lesot , Ayush K. Ray , Manal Saadi , and Fragkiskos D. Malliaros . 2019 . Semi-Supervised Learning and Graph Neural Networks for Fake News Detection. In International Conference on Advances in Social Networks Analysis and Mining. Adrien Benamira, Benjamin Devillers, Etienne Lesot, Ayush K. Ray, Manal Saadi, and Fragkiskos D. Malliaros. 2019. Semi-Supervised Learning and Graph Neural Networks for Fake News Detection. In International Conference on Advances in Social Networks Analysis and Mining.

3. Joan Bruna , Wojciech Zaremba , Arthur Szlam , and Yann LeCun . 2014 . Spectral networks and locally connected networks on graphs . In International Conference on Learning Representations. Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun. 2014. Spectral networks and locally connected networks on graphs. In International Conference on Learning Representations.

4. Graph Transformer for Graph-to-Sequence Learning

5. Rethinking Graph Neural Architecture Search from Message-passing

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