GraphStorm Advanced Usages

Multiple Target Node Types Training

When training on a hetergenious graph, we often need to train a model by minimizing the objective function on more than one node type. GraphStorm provides supports to achieve this goal.

Train on multiple node types: The users only need to edit the target_ntype in model config YAML file to minimize the objective function defined on mutiple target node types. For example, by setting target_ntype as following, we can jointly optimize the objective function defined on “movie” and “user” node types.
```
target_ntype:
-  movie
-  user
```
During evuation, the users can set a single node type for evaluation. For example, by setting eval_target_ntype: movie, we will only perform evaluation on “movie” node type.
Evaluate on single node type: During evuation, the users can set a single node type for evaluation. For example, by setting eval_target_ntype: movie, we will only perform evaluation on “movie” node type. Our current implementation only support evaluating on a single node type.
Per target node type decoder: The users may also want to use a different decoder on each node type, where the output dimension for each decoder maybe different. We can achieve this by setting num_classes in model config YAML file. For example, by setting num_classes as following, GraphStorm will create a decoder with output dimension as 3 for movie node type, and a decoder with output dimension as 7 for user node type.
```
num_classes:
  movie:  3
  user:  7
```
Reweighting on loss function: The users may also want to use a customized loss function reweighting on each node type, which can be achieved by setting multilabel, multilabel_weights, and imbalance_class_weights. Examples are illustrated as following. Our current implementation does not support different node types with different multilabel setting.
```
multilabel:
  movie:  true
  user:  true
multilabel_weights:
  movie:  0.1,0.2,0.3
  user:  0.1,0.2,0.3,0.4,0.5,0.0

multilabel:
  movie:  false
  user:  false
imbalance_class_weights:
  movie:  0.1,0.2,0.3
  user:  0.1,0.2,0.3,0.4,0.5,0.0
```