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Found 14 papers, 13 papers with code
E(n)-equivariant Graph Neural Cellular Automata
Cellular automata (CAs) are computational models exhibiting rich dynamics emerging from the local interaction of cells arranged in a regular lattice.
Generalised Implicit Neural Representations
We consider the problem of learning implicit neural representations (INRs) for signals on non-Euclidean domains.
Unsupervised Network Embedding Beyond Homophily
Here, we formulate the unsupervised NE task as an r-ego network discrimination problem and develop the SELENE framework for learning on networks with homophily and heterophily.
Learning Graph Cellular Automata
Cellular automata (CA) are a class of computational models that exhibit rich dynamics emerging from the local interaction of cells arranged in a regular lattice.
Understanding Pooling in Graph Neural Networks
Inspired by the conventional pooling layers in convolutional neural networks, many recent works in the field of graph machine learning have introduced pooling operators to reduce the size of graphs.
Graph Edit Networks
With this result, we hope to provide a firm theoretical basis for a next generation of time series prediction models.
Graph Neural Networks in TensorFlow and Keras with Spektral
In this paper we present Spektral, an open-source Python library for building graph neural networks with TensorFlow and the Keras application programming interface.
Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling
In graph neural networks (GNNs), pooling operators compute local summaries of input graphs to capture their global properties, and they are fundamental for building deep GNNs that learn hierarchical representations.
Ranked #1 on
Graph Classification
on Bench-hard
Mincut Pooling in Graph Neural Networks
For each node, our method learns a soft cluster assignment vector that depends on the node features, the target inference task (e. g., a graph classification loss), and, thanks to the minCut objective, also on the connectivity structure of the graph.
Spectral Clustering with Graph Neural Networks for Graph Pooling
Spectral clustering (SC) is a popular clustering technique to find strongly connected communities on a graph.
Autoregressive Models for Sequences of Graphs
This paper proposes an autoregressive (AR) model for sequences of graphs, which generalises traditional AR models.
Graph Neural Networks with convolutional ARMA filters
Popular graph neural networks implement convolution operations on graphs based on polynomial spectral filters.
Ranked #4 on
Skeleton Based Action Recognition
on SBU
Adversarial Autoencoders with Constant-Curvature Latent Manifolds
Constant-curvature Riemannian manifolds (CCMs) have been shown to be ideal embedding spaces in many application domains, as their non-Euclidean geometry can naturally account for some relevant properties of data, like hierarchy and circularity.
Change Detection in Graph Streams by Learning Graph Embeddings on Constant-Curvature Manifolds
A common approach is to use embedding techniques to represent graphs as points in a conventional Euclidean space, but non-Euclidean spaces have often been shown to be better suited for embedding graphs.
