图神经网络框架DGL入门介绍

图神经网络框架DGL学习——101

关于DGL

DGL是一个主流的开源的图神经网络，支持tensorflow， torch等语言，用的较多的是torch。具体介绍，请见官方主页：https://docs.dgl.ai/index.html

101（入门）

图神经网络的几个关键流程：
1.图的构建
2.特征传递给边或者节点
3.图神经网络模型的构建
4.模型训练
5.模型可视化

DGL可以帮助我们更快的建立一个图神经网络，主要体现在图的构建、特征赋予节点/边、自带各类图神经网络层、可视化上。以下是官方文档的入门教程代码。

一、图的构建

“Zachary’s karate club” 问题为例。Zachary’s karate club有34个成员，下图代表34个成员之间的社会联系，分裂成两个团体。已知0号成员和34号成员分别属于两个团体（黄色/红色）。需要根据34各成员的社会联系图，预测其他成员的团体归属。所以，这是一个节点层面的分类问题。

如何构建一个图呢？首先，找出每一个联系（边）的起始节点src和终止节点dst，分别形成数组，用于描述图中的关系。然后使用dgl.DGLGraph()函数构建图，代买如下：

import dgl
import numpy as np

def build_karate_club_graph():
    # All 78 edges are stored in two numpy arrays. One for source endpoints
    # while the other for destination endpoints.
    src = np.array([1, 2, 2, 3, 3, 3, 4, 5, 6, 6, 6, 7, 7, 7, 7, 8, 8, 9, 10, 10,
        10, 11, 12, 12, 13, 13, 13, 13, 16, 16, 17, 17, 19, 19, 21, 21,
        25, 25, 27, 27, 27, 28, 29, 29, 30, 30, 31, 31, 31, 31, 32, 32,
        32, 32, 32, 32, 32, 32, 32, 32, 32, 33, 33, 33, 33, 33, 33, 33,
        33, 33, 33, 33, 33, 33, 33, 33, 33, 33])
    dst = np.array([0, 0, 1, 0, 1, 2, 0, 0, 0, 4, 5, 0, 1, 2, 3, 0, 2, 2, 0, 4,
        5, 0, 0, 3, 0, 1, 2, 3, 5, 6, 0, 1, 0, 1, 0, 1, 23, 24, 2, 23,
        24, 2, 23, 26, 1, 8, 0, 24, 25, 28, 2, 8, 14, 15, 18, 20, 22, 23,
        29, 30, 31, 8, 9, 13, 14, 15, 18, 19, 20, 22, 23, 26, 27, 28, 29, 30,
        31, 32])
    # Edges are directional in DGL; Make them bi-directional.
    u = np.concatenate([src, dst])
    v = np.concatenate([dst, src])
    # Construct a DGLGraph
    return dgl.DGLGraph((u, v))

G = build_karate_club_graph()
print('We have %d nodes.' % G.number_of_nodes())
print('We have %d edges.' % G.number_of_edges())    

二、特征传递给边或者节点

在图神经网络中，特征是赋给边或者节点的。对于 “Zachary’s karate club” 问题，是要赋给节点。这里采用的是5维可训练的嵌入变量对34个节点进行赋值。

# In DGL, you can add features for all nodes at once, using a feature tensor that
# batches node features along the first dimension. The code below adds the learnable
# embeddings for all nodes:

import torch
import torch.nn as nn
import torch.nn.functional as F

embed = nn.Embedding(34, 5)  # 34 nodes with embedding dim equal to 5
G.ndata['feat'] = embed.weight


# print out node 2's input feature
print(G.ndata['feat'][2])

# print out node 10 and 11's input features
print(G.ndata['feat'][[10, 11]])

三、图神经网络模型的构建

这里基于GDL的GraphConv，构建一个简单的两层的图卷积神经网络。

from dgl.nn.pytorch import GraphConv
class GCN(nn.Module):
    def __init__(self, in_feats, hidden_size, num_classes):
        super(GCN, self).__init__()
        self.conv1 = GraphConv(in_feats, hidden_size)
        self.conv2 = GraphConv(hidden_size, num_classes)

    def forward(self, g, inputs):
        h = self.conv1(g, inputs)
        h = torch.relu(h)
        h = self.conv2(g, h)
        return h

# The first layer transforms input features of size of 5 to a hidden size of 5.
# The second layer transforms the hidden layer and produces output features of
# size 2, corresponding to the two groups of the karate club.
net = GCN(5, 5, 2)

四、模型训练

模型的数据输入就是刚才建立的可训练的嵌入向量。标签，由于只知道节点0和节点33的标签，而其他节点的标签并不清楚，所以应该是半监督学习问题。因此只能对节点33和节点0进行标记。

inputs = embed.weight
labeled_nodes = torch.tensor([0, 33])  # only the instructor and the president nodes are labeled
labels = torch.tensor([0, 1])  # their labels are different

模型训练：

import itertools

optimizer = torch.optim.Adam(itertools.chain(net.parameters(), embed.parameters()), lr=0.01)
all_logits = [] #用于记录训练过程中，各个节点的分类概率
for epoch in range(50):
    logits = net(G, inputs) #图卷积网络输出
    # we save the logits for visualization later
    all_logits.append(logits.detach())
    logp = F.log_softmax(logits, 1) #分类
    # we only compute loss for labeled nodes
    loss = F.nll_loss(logp[labeled_nodes], labels) #只计算已经标记节点的损失

    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    print('Epoch %d | Loss: %.4f' % (epoch, loss.item()))

五、模型可视化

使用netweokx进行。

图的可视化， 结果如下图：

import networkx as nx
# Since the actual graph is undirected, we convert it for visualization
# purpose.
nx_G = G.to_networkx().to_undirected()
# Kamada-Kawaii layout usually looks pretty for arbitrary graphs
pos = nx.kamada_kawai_layout(nx_G)
nx.draw(nx_G, pos, with_labels=True, node_color=[[.7, .7, .7]])

模型训练过程可视化：

import matplotlib.animation as animation
import matplotlib.pyplot as plt

def draw(i):
    cls1color = '#00FFFF'
    cls2color = '#FF00FF'
    pos = {}
    colors = []
    for v in range(34):
        pos[v] = all_logits[i][v].numpy()
        cls = pos[v].argmax()
        colors.append(cls1color if cls else cls2color)
    ax.cla()
    ax.axis('off')
    ax.set_title('Epoch: %d' % i)
    nx.draw_networkx(nx_G.to_undirected(), pos, node_color=colors,
            with_labels=True, node_size=300, ax=ax)

fig = plt.figure(dpi=150)
fig.clf()
ax = fig.subplots()
draw(0)  # draw the prediction of the first epoch
plt.close()

动态图片：

ani = animation.FuncAnimation(fig, draw, frames=len(all_logits), interval=200)

。。。不知道如何在CSDN中显示动态图，结果省略。
另外，在pycharm和jupyter notebook中动态图的显示，需要设置一下，否则显示不出来。
参考：https://blog.csdn.net/qq_42182596/article/details/106528274
https://www.jianshu.com/p/c6b362fde21c
当然，你一定找得到你的Python Scientific，好像社区版是没有的。。。。

本文地址：https://blog.csdn.net/wufeil7/article/details/107081483