- There are no experiments to support the claim that A-DGN can specifically alleviate/mitigate oversquashing.
- There are no experiments to support the claim that A-DGN can effectively handle long-range dependencies specifically on graph data requiring long-range reasoning.
- The poor long-range modelling ability of DGNs is attributed to oversquashing and vanishing/exploding gradients but the poor performance could also be due to oversmoothing, another phenomenon observed in the context of very deep graph networks [Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning, In AAAI'18].
- The poor long-range modelling ability of DGNs is attributed to oversquashing and vanishing/exploding gradients but the poor performance could also be due to oversmoothing, another phenomenon observed in the context of very deep graph networks [Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning, In AAAI'18].