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Graph Representation Learning Book
Introduction to graph representation learning, including methods for embedding graph data, graph neural networks, and deep generative models of graphs.
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Learning Representations via Graph-structured Networks
Introduce a series of effective graph-structured networks, including non-local neural networks, spatial generalized propagation networks, etc.
graph-neural-networks graph-structured-networks non-local-neural-networks spatial-generalized-propagation-networks
Deep Learning With Graph-Structured Representations
Novel approaches based on the theme of structuring the representations and computations of neural network-based models in the form of a graph.
graph-neural-networks graph-convolutional-networks graph-auto-encoders relational-graph-convolutional-networks
Discovering Symbolic Models from Deep Learning w/ Inductive Bias
A general approach to distill symbolic representations of a learned deep model by introducing strong inductive biases.
symbolic-models inductive-bias graph-neural-networks graphs
TF Geometric
Efficient and Friendly Graph Neural Network Library for TensorFlow 1.x and 2.x.
graph-neural-networks graph-convolutional-networks tensorflow code
On the Bottleneck of Graph Neural Networks and its Implications
The mechanism of propagating information between neighbors creates a bottleneck when every node aggregates messages from its neighbors.
graph-neural-networks bottleneck graphs video
Transformers are Graph Neural Networks
My engineering friends often ask me: deep learning on graphs sounds great, but are there any real applications?
transformers graph-neural-networks natural-language-processing article
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