Deep Graph Infomax (DGI) is a general approach for learning node representations within graph-structured data in an unsupervised manner. In contrast to most prior approaches to unsupervised learning with GCNs, DGI does not rely on random walk objectives, and is readily applicable to both transductive and inductive learning setups.
- Generalizing node-level representation learning is important, large-scale graph data is often with no labels. -> thus unsupervised representation learning method is much more important.
- Existing methods most rely on random walk-based objective, which
- over-emphasize proximity information at the expense of structural information
- is highly influenced by hyperparameter choice
- when introduced with stronger encoder, hard to tell whether the representation has meaningful signals.
- Deep InfoMax on image data, maximize the global/local mutual information. This encourages the encoder to carry the type of information that is present in all locations (and thus are globally relevant), such as would be the case of a class label.
- We are the first work applying it to the graph data.