EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs

Understand

Graph representation learning resurges as a trending research subject owing to the widespread use of deep learning for Euclidean data, which inspire various creative designs of neural networks in the non-Euclidean domain, particularly graphs.

• With the success of these graph neural networks (GNN) in the static setting, we approach further practical scenarios where the graph dynamically evolves.
• Existing approaches typically resort to node embeddings and use a recurrent neural network (RNN, broadly speaking) to regulate the embeddings and learn the temporal dynamics.
• These methods require the knowledge of a node in the full time span (including both training and testing) and are less applicable to the frequent change of the node set.