贯彻费曼学习法，用最简单的语句阐述复杂的理论
PYG JumpingKnowledge

JumpingKnowledge

GNN里也有JK了吗

思想：

非常简单。
假设有3层GNN，将经过GNN的每一层都保存下来，即中间层的每一层的嵌入保存，然后最后对所有层的嵌入列表做操作：
这里的操作有：拼接（cat）、最大池化（max pooling）、加权：

使用参数：

class JumpingKnowledge(mode: str, channels: Optional[int] = None, num_layers: Optional[int] = None) 

mode (str) – The aggregation scheme to use (“cat”, “max” or “lstm”).

channels (int, optional) – The number of channels per representation. Needs to be only set for LSTM-style aggregation. (default: None)

num_layers (int, optional) – The number of layers to aggregate. Needs to be only set for LSTM-style aggregation. (default: None)

举例

import torch
import torch.nn.functional as F
from torch_geometric.nn import GCNConv, JumpingKnowledge

class GNN(torch.nn.Module):
    def __init__(self, in_channels, hidden_channels, out_channels, num_layers, jk_mode):
        super(GNN, self).__init__()
        self.convs = torch.nn.ModuleList()
        self.convs.append(GCNConv(in_channels, hidden_channels))
        for _ in range(num_layers - 1):
            self.convs.append(GCNConv(hidden_channels, hidden_channels))
        self.jk = JumpingKnowledge(jk_mode, channels=hidden_channels, num_layers=num_layers)
        self.lin = torch.nn.Linear(hidden_channels, out_channels)

    def forward(self, x, edge_index):
        xs = []
        for conv in self.convs:
            x = F.relu(conv(x, edge_index))
            xs.append(x)
        x = self.jk(xs)
        x = self.lin(x)
        return F.log_softmax(x, dim=1)

# 示例用法
model = GNN(in_channels=16, hidden_channels=32, out_channels=7, num_layers=3, jk_mode='max')
print(model)

Jumping Knowledge 机制通过合并不同层的节点表示，解决了图神经网络中的一些关键问题，提高了模型的表达能力和性能。其多种合并模式提供了灵活性