README.txt

.. _tutorials2-index:


Dealing with many small graphs
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* **Tree-LSTM** `[paper] <https://arxiv.org/abs/1503.00075>`__ `[tutorial] <models/3_tree-lstm.html>`__
  `[code] <https://github.com/jermainewang/dgl/blob/master/examples/pytorch/tree_lstm/tree_lstm.py>`__:
  sentences of natural languages have inherent structures, which are thrown away
  by treating them simply as sequences. Tree-LSTM is a powerful model that learns
  the representation by leveraging prior syntactic structures (e.g. parse-tree).
  The challenge to train it well is that simply by padding a sentence to the
  maximum length no longer works, since trees of different sentences have
  different sizes and topologies. DGL solves this problem by throwing the trees
  into a bigger "container" graph, and use message-passing to explore maximum
  parallelism. The key API we use is batching.