Learning to Evolve on Dynamic Graphs
This work addresses the challenge of modeling dynamic graphs for applications like social networks or recommendation systems, offering a model-agnostic solution with incremental improvements over existing methods.
The paper tackles representation learning on dynamic graphs by proposing LEDG, a gradient-based meta-learning approach that jointly learns graph and temporal information, achieving better generalization than RNN-based methods on various datasets and tasks.
Representation learning in dynamic graphs is a challenging problem because the topology of graph and node features vary at different time. This requires the model to be able to effectively capture both graph topology information and temporal information. Most existing works are built on recurrent neural networks (RNNs), which are used to exact temporal information of dynamic graphs, and thus they inherit the same drawbacks of RNNs. In this paper, we propose Learning to Evolve on Dynamic Graphs (LEDG) - a novel algorithm that jointly learns graph information and time information. Specifically, our approach utilizes gradient-based meta-learning to learn updating strategies that have better generalization ability than RNN on snapshots. It is model-agnostic and thus can train any message passing based graph neural network (GNN) on dynamic graphs. To enhance the representation power, we disentangle the embeddings into time embeddings and graph intrinsic embeddings. We conduct experiments on various datasets and down-stream tasks, and the experimental results validate the effectiveness of our method.