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Found 7 papers, 2 papers with code
Graph Neural Networks with a Distribution of Parametrized Graphs
Traditionally, graph neural networks have been trained using a single observed graph.
Leveraging Label Non-Uniformity for Node Classification in Graph Neural Networks
We introduce the key notion of label non-uniformity, which is derived from the Wasserstein distance between the softmax distribution of the logits and the uniform distribution.
Distributional Signals for Node Classification in Graph Neural Networks
In graph neural networks (GNNs), both node features and labels are examples of graph signals, a key notion in graph signal processing (GSP).
Node-Specific Space Selection via Localized Geometric Hyperbolicity in Graph Neural Networks
However, a graph can have hyperbolic and Euclidean geometries at different regions of the graph.
On semi shift invariant graph filters
In graph signal processing, one of the most important subjects is the study of filters, i. e., linear transformations that capture relations between graph signals.
SGAT: Simplicial Graph Attention Network
In this paper, we present Simplicial Graph Attention Network (SGAT), a simplicial complex approach to represent such high-order interactions by placing features from non-target nodes on the simplices.
Learning on heterogeneous graphs using high-order relations
A heterogeneous graph consists of different vertices and edges types.
