Graph neural networks and its variantsο
Graph convolutional network (GCN) [research paper] [tutorial] [Pytorch code] [MXNet code]:
Graph attention network (GAT) [research paper] [tutorial] [Pytorch code] [MXNet code]: GAT extends the GCN functionality by deploying multi-head attention among neighborhood of a node. This greatly enhances the capacity and expressiveness of the model.
Relational-GCN [research paper] [tutorial] [Pytorch code] [MXNet code]: Relational-GCN allows multiple edges among two entities of a graph. Edges with distinct relationships are encoded differently.
Line graph neural network (LGNN) [research paper] [tutorial] [Pytorch code]: This network focuses on community detection by inspecting graph structures. It uses representations of both the original graph and its line-graph companion. In addition to demonstrating how an algorithm can harness multiple graphs, this implementation shows how you can judiciously mix simple tensor operations and sparse-matrix tensor operations, along with message-passing with DGL.
