.. _guide-training:


Chapter 5: Training Graph Neural Networks
=====================================================

Overview
--------

This chapter discusses how to train a graph neural network for node
classification, edge classification, link prediction, and graph
classification for small graph(s), by message passing methods introduced
in :ref:`guide-message-passing` and neural network modules introduced in
:ref:`guide-nn`.

This chapter assumes that your graph as well as all of its node and edge
features can fit into GPU; see :ref:`guide-minibatch` if they cannot.

The following text assumes that the graph(s) and node/edge features are
already prepared. If you plan to use the dataset DGL provides or other
compatible ``DGLDataset`` as is described in :ref:`guide-data-pipeline`, you can
get the graph for a single-graph dataset with something like

.. code:: python

    import dgl
    
    dataset = dgl.data.CiteseerGraphDataset()
    graph = dataset[0]


Note: In this chapter we will use PyTorch as backend.

Heterogeneous Graphs
~~~~~~~~~~~~~~~~~~~~

Sometimes you would like to work on heterogeneous graphs. Here we take a
synthetic heterogeneous graph as an example for demonstrating node
classification, edge classification, and link prediction tasks.

The synthetic heterogeneous graph ``hetero_graph`` has these edge types:

-  ``('user', 'follow', 'user')``
-  ``('user', 'followed-by', 'user')``
-  ``('user', 'click', 'item')``
-  ``('item', 'clicked-by', 'user')``
-  ``('user', 'dislike', 'item')``
-  ``('item', 'disliked-by', 'user')``

.. code:: python

    import numpy as np
    import torch
    
    n_users = 1000
    n_items = 500
    n_follows = 3000
    n_clicks = 5000
    n_dislikes = 500
    n_hetero_features = 10
    n_user_classes = 5
    n_max_clicks = 10
    
    follow_src = np.random.randint(0, n_users, n_follows)
    follow_dst = np.random.randint(0, n_users, n_follows)
    click_src = np.random.randint(0, n_users, n_clicks)
    click_dst = np.random.randint(0, n_items, n_clicks)
    dislike_src = np.random.randint(0, n_users, n_dislikes)
    dislike_dst = np.random.randint(0, n_items, n_dislikes)
    
    hetero_graph = dgl.heterograph({
        ('user', 'follow', 'user'): (follow_src, follow_dst),
        ('user', 'followed-by', 'user'): (follow_dst, follow_src),
        ('user', 'click', 'item'): (click_src, click_dst),
        ('item', 'clicked-by', 'user'): (click_dst, click_src),
        ('user', 'dislike', 'item'): (dislike_src, dislike_dst),
        ('item', 'disliked-by', 'user'): (dislike_dst, dislike_src)})
    
    hetero_graph.nodes['user'].data['feature'] = torch.randn(n_users, n_hetero_features)
    hetero_graph.nodes['item'].data['feature'] = torch.randn(n_items, n_hetero_features)
    hetero_graph.nodes['user'].data['label'] = torch.randint(0, n_user_classes, (n_users,))
    hetero_graph.edges['click'].data['label'] = torch.randint(1, n_max_clicks, (n_clicks,)).float()
    # randomly generate training masks on user nodes and click edges
    hetero_graph.nodes['user'].data['train_mask'] = torch.zeros(n_users, dtype=torch.bool).bernoulli(0.6)
    hetero_graph.edges['click'].data['train_mask'] = torch.zeros(n_clicks, dtype=torch.bool).bernoulli(0.6)


Roadmap
------------

The chapter has four sections, each for one type of graph learning tasks.

* :ref:`guide-training-node-classification`
* :ref:`guide-training-edge-classification`
* :ref:`guide-training-link-prediction`
* :ref:`guide-training-graph-classification`

.. toctree::
    :maxdepth: 1
    :hidden:
    :glob:

    training-node
    training-edge
    training-link
    training-graph
