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(WIP) K3 Node: Multi-Backend Graph Neural Networks

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Ported from Spektral, PyTorch Geometric and StellarGraph

Important

K3 Node only supports operations on sparse adjacency matrix with TensorFlow backend. Support for JAX or PyTorch backends will be added future releases.

What's Included:

Convolution Layers

  • AGNNConv: An Attention-based Graph Neural Network (AGNN)
  • APPNPConv PPNPPropagation: (Approximate) Personalized Propagation of Neural Predictions (PPNP/APPNP)
  • ARMAConv: An Auto-Regressive Moving Average convolutional layer (ARMA) from the paper
  • CrystalConv: Crystal Graph Convolutional Neural
  • GatedGraphConv: Gated Graph Sequence Neural Networks
  • GraphConvolution: Graph Convolutional Network (GCN)
  • GeneralConv: A general convolutional layer from the paper: Design Space for Graph Neural Networks
  • GINConv: A Graph Isomorphism Network (GIN) from the paper: How Powerful are Graph Neural Networks?
  • GraphAttention: Graph ATtention Network (GAT)
  • SAGEConv: A GraphSAGE layer from the paper: Inductive Representation Learning on Large Graphs

Aggregation Layers

  • SumAggregation
  • MaxAggregation
  • MeanAggregation
  • SoftmaxAggregation
  • PowerMeanAggregation

Normalization Layers

  • MeanSubtractionNorm
  • MessageNorm

Attention Layers:

  • Performer: The linear scaled attention mechanism from the "Rethinking Attention with Performers"

Examples

TensorFlow Backend

  • Model based on ARMAConv using features from spektral Open In Colab

PyTorch Backend

  • Model based on GatedGraphConv using features from torch_geometric Open In Colab

Contributions

Help us improve K3Node! Report issues, submit patches, or share your suggestions.