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Found 6 papers, 2 papers with code
Generalization Bounds for Message Passing Networks on Mixture of Graphons
In this more realistic and challenging scenario, we provide a generalization bound that decreases as the average number of nodes in the graphs increases.
Weisfeiler and Leman Go Loopy: A New Hierarchy for Graph Representational Learning
We introduce $r$-loopy Weisfeiler-Leman ($r$-$\ell{}$WL), a novel hierarchy of graph isomorphism tests and a corresponding GNN framework, $r$-$\ell{}$MPNN, that can count cycles up to length $r + 2$.
A Fractional Graph Laplacian Approach to Oversmoothing
In this paper, we generalize the concept of oversmoothing from undirected to directed graphs.
Generalized Laplacian Positional Encoding for Graph Representation Learning
Graph neural networks (GNNs) are the primary tool for processing graph-structured data.
Generalization Analysis of Message Passing Neural Networks on Large Random Graphs
Message passing neural networks (MPNN) have seen a steep rise in popularity since their introduction as generalizations of convolutional neural networks to graph-structured data, and are now considered state-of-the-art tools for solving a large variety of graph-focused problems.
Transferability of Graph Neural Networks: an Extended Graphon Approach
Our main contributions can be summarized as follows: 1) we prove that any fixed GCNN with continuous filters is transferable under graphs that approximate the same graphon, 2) we prove transferability for graphs that approximate unbounded graphon shift operators, which are defined in this paper, and, 3) we obtain non-asymptotic approximation results, proving linear stability of GCNNs.
