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zhjwy9343 90d86fcbe3 Master refactor split chapter2n3 (#2215)
* [Feature] Add full graph training with dgl built-in dataset.

* [Feature] Add full graph training with dgl built-in dataset.

* [Feature] Add full graph training with dgl built-in dataset.

* [Bug] fix model to cuda.

* [Bug] fix model to cuda.

* [Bug] fix model to cuda.

* [Bug] fix model to cuda.

* [Bug] fix model to cuda.

* [Bug] fix model to cuda.

* [Bug] fix model to cuda.

* [Bug] fix model to cuda.

* [Bug] fix model to cuda.

* [Bug] fix model to cuda.

* [Bug] fix model to cuda.

* [Bug] fix model to cuda.

* [Bug] fix model to cuda.

* [Bug] fix model to cuda.

* [Bug] fix model to cuda.

* [Bug] fix model to cuda.

* [Bug] fix model to cuda.

* [Bug] fix model to cuda.

* [Bug] fix model to cuda.

* [Bug] fix model to cuda.

* [Bug] fix model to cuda.

* [Feature] Add test loss and accuracy

* [Feature] Add test loss and accuracy

* [Feature] Add test loss and accuracy

* [Feature] Add test loss and accuracy

* [Feature] Add test loss and accuracy

* [Feature] Add test loss and accuracy

* [Fix] Add random

* [Bug] Fix batch norm error

* [Doc] Test with CN in Sphinx

* [Doc] Test with CN in Sphinx

* [Doc] Remove the test CN docs.

* [Feature] Add input embedding layer

* [Feature] Add input embedding layer

* [Feature] Add input embedding layer

* [Feature] Add input embedding layer

* [Feature] Add input embedding layer

* [Feature] Add input embedding layer

* [Feature] Add input embedding layer

* [Feature] Add input embedding layer

* [Feature] Add input embedding layer

* [Doc] fill readme with new performance results

* [Doc] Add Chinese User Guide, graph and 1.5

* [Doc] Add Chinese User Guide, graph and 1.5

* [Doc] Refactor and split chapter 4

* [Fix] Remove CompGCN example codes

* [Doc] Add chapter 2 refactor and split

* [Fix] code format of savenload

* [Doc] Split chapter 3

* [Doc] Add introduction phrase of chapter 2

* [Doc] Add introduction phrase of chapter 2

* [Doc] Add introduction phrase of chapter 3

* Fix

* Update chapter 2

* Update chapter 3

* Update chapter 4

Co-authored-by: mufeili <mufeili1996@gmail.com>
2020-09-20 17:45:35 +08:00

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.. _guide-message-passing:
Chapter 2: Message Passing
==========================
Message Passing Paradigm
------------------------
Let :math:`x_v\in\mathbb{R}^{d_1}` be the feature for node :math:`v`,
and :math:`w_{e}\in\mathbb{R}^{d_2}` be the feature for edge
:math:`({u}, {v})`. The **message passing paradigm** defines the
following node-wise and edge-wise computation at step :math:`t+1`:
.. math:: \text{Edge-wise: } m_{e}^{(t+1)} = \phi \left( x_v^{(t)}, x_u^{(t)}, w_{e}^{(t)} \right) , ({u}, {v},{e}) \in \mathcal{E}.
.. math:: \text{Node-wise: } x_v^{(t+1)} = \psi \left(x_v^{(t)}, \rho\left(\left\lbrace m_{e}^{(t+1)} : ({u}, {v},{e}) \in \mathcal{E} \right\rbrace \right) \right).
In the above equations, :math:`\phi` is a **message function**
defined on each edge to generate a message by combining the edge feature
with the features of its incident nodes; :math:`\psi` is an
**update function** defined on each node to update the node feature
by aggregating its incoming messages using the **reduce function**
:math:`\rho`.
Roadmap
-------
This chapter introduces DGL's message passing APIs, and how to efficiently use them on both nodes and edges.
The last section of it explains how to implement message passing on heterogeneous graphs.
* :ref:`guide-message-passing-api`
* :ref:`guide-message-passing-efficient`
* :ref:`guide-message-passing-part`
* :ref:`guide-message-passing-edge`
* :ref:`guide-message-passing-heterograph`
.. toctree::
:maxdepth: 1
:hidden:
:glob:
message-api
message-efficient
message-part
message-edge
message-heterograph