wehub-resource-sync
112 lines
3.7 KiB
ReStructuredText
112 lines
3.7 KiB
ReStructuredText
.. _guide-training:
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Chapter 5: Training Graph Neural Networks
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=====================================================
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:ref:`(中文版) <guide_cn-training>`
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Overview
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--------
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This chapter discusses how to train a graph neural network for node
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classification, edge classification, link prediction, and graph
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classification for small graph(s), by message passing methods introduced
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in :ref:`guide-message-passing` and neural network modules introduced in
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:ref:`guide-nn`.
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This chapter assumes that your graph as well as all of its node and edge
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features can fit into GPU; see :ref:`guide-minibatch` if they cannot.
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The following text assumes that the graph(s) and node/edge features are
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already prepared. If you plan to use the dataset DGL provides or other
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compatible ``DGLDataset`` as is described in :ref:`guide-data-pipeline`, you can
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get the graph for a single-graph dataset with something like
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.. code:: python
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import dgl
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dataset = dgl.data.CiteseerGraphDataset()
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graph = dataset[0]
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Note: In this chapter we will use PyTorch as backend.
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.. _guide-training-heterogeneous-graph-example:
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Heterogeneous Graphs
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~~~~~~~~~~~~~~~~~~~~
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Sometimes you would like to work on heterogeneous graphs. Here we take a
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synthetic heterogeneous graph as an example for demonstrating node
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classification, edge classification, and link prediction tasks.
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The synthetic heterogeneous graph ``hetero_graph`` has these edge types:
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- ``('user', 'follow', 'user')``
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- ``('user', 'followed-by', 'user')``
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- ``('user', 'click', 'item')``
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- ``('item', 'clicked-by', 'user')``
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- ``('user', 'dislike', 'item')``
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- ``('item', 'disliked-by', 'user')``
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.. code:: python
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import numpy as np
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import torch
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n_users = 1000
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n_items = 500
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n_follows = 3000
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n_clicks = 5000
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n_dislikes = 500
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n_hetero_features = 10
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n_user_classes = 5
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n_max_clicks = 10
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follow_src = np.random.randint(0, n_users, n_follows)
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follow_dst = np.random.randint(0, n_users, n_follows)
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click_src = np.random.randint(0, n_users, n_clicks)
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click_dst = np.random.randint(0, n_items, n_clicks)
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dislike_src = np.random.randint(0, n_users, n_dislikes)
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dislike_dst = np.random.randint(0, n_items, n_dislikes)
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hetero_graph = dgl.heterograph({
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('user', 'follow', 'user'): (follow_src, follow_dst),
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('user', 'followed-by', 'user'): (follow_dst, follow_src),
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('user', 'click', 'item'): (click_src, click_dst),
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('item', 'clicked-by', 'user'): (click_dst, click_src),
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('user', 'dislike', 'item'): (dislike_src, dislike_dst),
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('item', 'disliked-by', 'user'): (dislike_dst, dislike_src)})
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hetero_graph.nodes['user'].data['feature'] = torch.randn(n_users, n_hetero_features)
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hetero_graph.nodes['item'].data['feature'] = torch.randn(n_items, n_hetero_features)
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hetero_graph.nodes['user'].data['label'] = torch.randint(0, n_user_classes, (n_users,))
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hetero_graph.edges['click'].data['label'] = torch.randint(1, n_max_clicks, (n_clicks,)).float()
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# randomly generate training masks on user nodes and click edges
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hetero_graph.nodes['user'].data['train_mask'] = torch.zeros(n_users, dtype=torch.bool).bernoulli(0.6)
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hetero_graph.edges['click'].data['train_mask'] = torch.zeros(n_clicks, dtype=torch.bool).bernoulli(0.6)
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Roadmap
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------------
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The chapter has four sections, each for one type of graph learning tasks.
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* :ref:`guide-training-node-classification`
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* :ref:`guide-training-edge-classification`
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* :ref:`guide-training-link-prediction`
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* :ref:`guide-training-graph-classification`
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* :ref:`guide-training-eweight`
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.. toctree::
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:maxdepth: 1
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:hidden:
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:glob:
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training-node
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training-edge
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training-link
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training-graph
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training-eweight
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