graph-neural-networks

🚀 Feature

Motivation

paper "LEARNING TO REPRESENT PROGRAMS WITH GRAPHS" which encode computer programs as graphs, with rich semantic information, however, most code implementation on this dataset VarMisuse is based on TensorFlow, like [tf-gnn-samples](https://github.com/microsof

Description

Currently our unit tests are disorganized and each test creates example StellarGraph graphs in different or similar ways with no sharing of this code.

This issue is to improve the unit tests by making functions to create example graphs available to all unit tests by, for example, making them pytest fixtures at the top level of the tests (see https://docs.pytest.org/en/latest/

We have several built-in datasets that can be easily loaded in one-line, located in the dataset directory of Aliyun OSS bucket graphscope, and the corresponding utility function to load them, located in python/graphscope/dataset/. We are planning to enrich the datasets continuously.

There's the procedure to add new datasets:

1. Find a popular and appropriate dataset, adapt the format to

想学习源码，请问应该从哪里看起。
1、目前我对应着论文里的storage、sampling、operator几个部分在core里找，但始终难以串起来；
2、另外分布式相关的也不知道怎么看，比如storage如何把图数据在分布式环境中存储；
官方的文档感觉和论文难以关联起来，分布式的也只看到了一个k8s训练的例子。对于源码学习这块，有人可以指导一下吗