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基于torch框架,可以使用torch_geometric.nn中MessagePassing,通过自己定义propagate,aggregate和update函数实现图神经网络的计算(例子:链接中的line 201-345)。使用paddle框架,基于PGL是否可以实现类似例子的功能?
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有的,我们就是通过send(对应propagte)、recv(对应aggregate)来定义图神经网路的,在nn.conv里面也有很多对应的例子
https://github.com/PaddlePaddle/PGL/blob/main/pgl/nn/conv.py#L298
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基于torch框架,可以使用torch_geometric.nn中MessagePassing,通过自己定义propagate,aggregate和update函数实现图神经网络的计算(例子:链接中的line 201-345)。使用paddle框架,基于PGL是否可以实现类似例子的功能?
The text was updated successfully, but these errors were encountered: