05 December 2025
Graph Neural Networks (GNNs) are models that leverage the graph structure to transmit information between nodes, typically through the message-passing operation. While widely successful, this approach is well-known to suffer from representational collapse as the number of layers increases and insensitivity to the information contained at distant and poorly connected nodes.
In this study, EMERGE partners from the University of Pisa present a unified view of on the appearance of these issues through the lens of vanishing gradients, using ideas from linear control theory for their analysis. Thea authors propose an interpretation of GNNs as recurrent models and empirically demonstrate that a simple state-space formulation of an GNN effectively alleviates these issues at no extra trainable parameter cost.
Read the paper in the link below.
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