Question 1. DeepSNAP异质图简介

表示异质图所需的图属性：

node_feature: 节点特征The feature of each node (torch.tensor)
edge_feature: 边特征The feautre of each edge (torch.tensor)
node_label: 节点标签The label of each node (int)
node_type: 节点类型The node type of each node (string)
edge_type: 边类型The edge type of each edge (string)

在question 1部分，我们将使用图数据集karate club network作为示例。对该数据的介绍可参考我之前写的笔记：图数据集Zachary‘s karate club network详解，包括其在NetworkX、PyG上的获取和应用方式_诸神缄默不语的博客-CSDN博客

首先获取图数据，并按照其不同的类别（指所属club的不同）实现可视化：

from pylab import *
import networkx as nx
from networkx.algorithms.community import greedy_modularity_communities
import matplotlib.pyplot as plt
import copy
G = nx.karate_club_graph()
community_map = {}  #key是节点索引，value是所属community的索引（0或1）
for node in G.nodes(data=True):
  #node第一个元素是索引，第二个元素是相关数据，如在本例中就是{'club': 'Mr. Hi'}
  #默认data=False，就只输出索引
  if node[1]["club"] == "Mr. Hi":
    community_map[node[0]] = 0
  else:
    community_map[node[0]] = 1
node_color = []
color_map = {0: 0, 1: 1}
node_color = [color_map[community_map[node]] for node in G.nodes()]
pos = nx.spring_layout(G)  #见下文介绍
plt.figure(figsize=(7, 7))
nx.draw(G, pos=pos, cmap=plt.get_cmap('coolwarm'), node_color=node_color)
show()

关于 nx.spring_layout(G)：这个是一个用来排布节点的函数，可以美化图可视化图像。

函数文档见：networkx.drawing.layout.spring_layout — NetworkX 2.6.2 documentation

大致功能是输入图数据等参数，返回以节点索引为key、节点对应的坐标为value的dict，dict元素示例：0: array([ 0.42143337, -0.10723518])

排布算法为Fruchterman-Reingold force-directed algorithm2，大致是模拟这样的逻辑：将边视为使所连接节点靠近的弹簧，而节点彼此之间有斥力，模拟演化到平衡状态时的布局。

这个的返回值可以置入 nx.draw() 的入参 pos 中，就让所绘制的图节点按这个字典的坐标来布局。

1.1 Question 1.1：分配Node Type and Node Features

用字典 community_map 和图 G 向 G 中增加 node_type 和 node_label 属性：对属于 “Mr. Hi” 俱乐部的节点赋 n0 为 node type、0 为 node label，对属于 “Officer” 俱乐部的节点赋 n1 为 node type、1为 node label。

给所有节点赋特征 [1, 1, 1, 1, 1]。

参考的NetworkX函数 nx.classes.function.set_node_attributes 文档：networkx.classes.function.set_node_attributes — NetworkX 2.6.2 documentation

函数使用示例：

G_eg = nx.path_graph(3)
bb = nx.betweenness_centrality(G)  #bb是一个字典
nx.set_node_attributes(G_eg, bb, "betweenness")
G_eg.nodes[1]["betweenness"]

0.053936688311688304

问题答案代码：

import torch
def assign_node_types(G, community_map):
  """
  输入NetworkX图G和community map（将节点映射到0/1标签的字典）
  在G中增加node_type这一节点属性
  """
  new_cm={}
  for (k,v) in community_map.items():
    if v==0:
      new_cm[k]='n0'
    else:
      new_cm[k]='n1'
  #我参考的答案里另一种比较优雅的写法：
  #node_type_map = {0:'n0', 1:'n1'}
  #node_types = {node:node_type_map[community_map[node]] for node in G.nodes()}
  nx.set_node_attributes(G,new_cm,'node_type')
def assign_node_labels(G, community_map):
  """
  输入NetworkX图G和community map（将节点映射到0/1标签的字典）
  在G中增加node_label这一节点属性
  """
  nx.set_node_attributes(G,community_map,'node_label')
def assign_node_features(G):
  """
  输入NetworkX图G
  在G中增加node_feature这一节点属性
  """
  feature_vector=[1, 1, 1, 1, 1]
  nx.set_node_attributes(G,feature_vector,'node_feature')
assign_node_types(G, community_map)
assign_node_labels(G, community_map)
assign_node_features(G)

验证函数效果的代码：

for n in G.nodes(data=True):
    print(n)
    break

(0, {‘club’: ‘Mr. Hi’, ‘node_type’: ‘n0’, ‘node_label’: 0, ‘node_feature’: [1, 1, 1, 1, 1]})

1.2 Question 1.2：分配Edge Types

分配标准：

Edges within club “Mr. Hi”: e0
Edges within club “Officer”: e1
Edges between clubs: e2

参考的NetworkX函数 nx.classes.function.set_edge_attributes 文档：networkx.classes.function.set_edge_attributes — NetworkX 2.6.2 documentation

问题答案代码：

def assign_edge_types(G, community_map):
  """
  输入NetworkX图G和community map（将节点映射到0/1标签的字典）
  在G中增加edge_type这一边属性
  """
  #注：我觉得题目原来的意思是让用community_map赋值的，但用club属性应该也无所谓……
  edge2attr_map={}
  for edge in G.edges():
    if G.nodes[edge[0]]['club']=='Mr. Hi' and G.nodes[edge[1]]['club']=='Mr. Hi':
      edge2attr_map[edge]='e0'
    elif G.nodes[edge[0]]['club']=='Officer' and G.nodes[edge[1]]['club']=='Officer':
      edge2attr_map[edge]='e1'
    else:
      edge2attr_map[edge]='e2'
  nx.set_edge_attributes(G,edge2attr_map,'edge_type')
assign_edge_types(G, community_map)

验证函数效果的代码：

#PRW
for edge in G.edges(data=True):
    print(edge)
    break

(0, 1, {‘edge_type’: ‘e0’})

1.3 NetworkX异质图可视化

edge_color = {}
for edge in G.edges():
  n1, n2 = edge
  if community_map[n1] == community_map[n2] and community_map[n1] == 0:
    edge_color[edge] = 'blue'
  elif community_map[n1] == community_map[n2] and community_map[n1] == 1:
    edge_color[edge] = 'red'
  else:
    edge_color[edge] = 'green'
G_orig = copy.deepcopy(G)
nx.classes.function.set_edge_attributes(G, edge_color, name='color')
colors = nx.get_edge_attributes(G,'color').values()
labels = nx.get_node_attributes(G, 'node_type')
plt.figure(figsize=(8, 8))
nx.draw(G, pos=pos, cmap=plt.get_cmap('coolwarm'), node_color=node_color, edge_color=colors, labels=labels, font_color='white')
show()

1.4 将NetworkX异质图转换为DeepSNAP异质图

from deepsnap.hetero_graph import HeteroGraph
hete = HeteroGraph(G_orig)

呃注意这部分代码有点难伺候，如果用 G 作为NetworkX backend，就会报 TypeError: Unknown type color in edge attributes. 这个错。

我看了一下对应的源代码：deepsnap.hetero_graph — DeepSNAP 0.2.0 documentation，就发现事情是这样的：

G_orig 的节点属性：

G_orig.nodes(data=True)[0]

输出：

{'club': 'Mr. Hi',
 'node_type': 'n0',
 'node_label': 0,
 'node_feature': [1, 1, 1, 1, 1]}

G_orig 的边属性：

for e in G_orig.edges(data=True):
    print(e)
    break

输出：

(0, 1, {'edge_type': 'e0'})

G 的边属性：

for e in G.edges(data=True):
    print(e)
    break

输出：

(0, 1, {'edge_type': 'e0', 'color': 'blue'})

DeepSNAP中对应的代码：

def _get_edge_attributes(self, key: str):
    r"""
    Similar to the `_get_node_attributes`
    """
    attributes = {}
    indices = None
    # TODO: suspect edge_to_tensor_mapping and edge_to_graph_mapping not useful
    if key == "edge_type":
        indices = {}
    for edge_idx, (head, tail, edge_dict) in enumerate(
        self.G.edges(data=True)
    ):
        if key in edge_dict:
            head_type = self.G.nodes[head]["node_type"]
            tail_type = self.G.nodes[tail]["node_type"]
            edge_type = self._get_edge_type(edge_dict)
            message_type = (head_type, edge_type, tail_type)
            if message_type not in attributes:
                attributes[message_type] = []
            attributes[message_type].append(edge_dict[key])
            if indices is not None:
                if message_type not in indices:
                    indices[message_type] = []
                indices[message_type].append(edge_idx)
    if len(attributes) == 0:
        return None
    for message_type, val in attributes.items():
        if torch.is_tensor(attributes[message_type][0]):
            attributes[message_type] = torch.stack(val, dim=0)
        elif isinstance(attributes[message_type][0], float):
            attributes[message_type] = torch.tensor(val, dtype=torch.float)
        elif isinstance(attributes[message_type][0], int):
            attributes[message_type] = torch.tensor(val, dtype=torch.long)
        elif (
            isinstance(attributes[message_type][0], str)
            and key == "edge_type"
        ):
            continue
        else:
            raise TypeError(f"Unknown type {key} in edge attributes.")

总之简单来说就是除了edge_type之外，边属性都不能是str格式。所以color这个属性就会报错。

但这样我们就很容易产生质疑，那节点属性里面的 club 又是怎么回事呢？然后我简单看了一下 _get_node_attributes() 这个函数，发现反正它没有边属性的那种限制……

我不确定是作者写这玩意时候没整明白，还是我妹整明白，我暂时也懒得问了。如果以后需要用DeepSNAP再去研究。

总之有这么个情况，在此说明。

可以打印出异质图的属性看一下：

for hetero_feature in hete:
    print(hetero_feature)

输出略

1.5 Question1.3：每一node type有多少个节点

hete的note_type属性是一个字典，key为node_type值（如 n0），如果key是str则value为类似这样的list：['n0', 'n0', 'n0', 'n0', 'n0', 'n0', 'n0', 'n0', 'n0', 'n0', 'n0', 'n0', 'n0', 'n0', 'n0', 'n0', 'n0']；如果key是int则value为Tensor。

def get_nodes_per_type(hete):
  num_nodes_n0=len(hete.node_type['n0'])
  num_nodes_n1=len(hete.node_type['n1'])
  return num_nodes_n0, num_nodes_n1
num_nodes_n0, num_nodes_n1 = get_nodes_per_type(hete)
print("Node type n0 has {} nodes".format(num_nodes_n0))
print("Node type n1 has {} nodes".format(num_nodes_n1))

输出：

Node type n0 has 17 nodes
Node type n1 has 17 nodes

1.6 Question 1.4：每一message type有多少条边

message type是node type和edge type的结合体。

hete.message_types

输出：

[('n0', 'e0', 'n0'), ('n0', 'e2', 'n1'), ('n1', 'e1', 'n1')]

edge_type是键为message_type值的字典，某一元素示例：

hete.edge_type[('n0', 'e0', 'n0')]

输出是一个元素全为 'e0' 的列表，具体略

问题答案代码：

def get_num_message_edges(hete):
  """
  返回一个列表，元素为tuple(message_type, num_edge)
  """
  message_type_edges = []
  for message_type,num_edge in hete.edge_type.items():
    message_type_edges.append((message_type,len(num_edge)))
  return message_type_edges
message_type_edges = get_num_message_edges(hete)
for (message_type, num_edges) in message_type_edges:
  print("Message type {} has {} edges".format(message_type, num_edges))

输出：

Message type ('n0', 'e0', 'n0') has 35 edges
Message type ('n0', 'e2', 'n1') has 11 edges
Message type ('n1', 'e1', 'n1') has 32 edges

1.7 Question 1.5：数据集划分：每一个split中有多少个节点？

DeepSNAP有内置的数据集划分函数。

问题答案代码：

from deepsnap.dataset import GraphDataset
def compute_dataset_split_counts(datasets):
  """
  入参：数据集划分后得到的字典（key为'train'/'val'/'test'，value为对应的GraphSataset）
  返回值：字典（key为'train'/'val'/'test'，value为对应split中含有的有标签节点个数）
  """
  data_set_splits = {}
  for ds_name,ds in datasets.items():
    #print(ds_name)  train
    #print(ds[0].node_label_index)  {'n0': tensor([10,  8,  3, 12,  0, 13]), 'n1': tensor([ 0,  8,  1, 15,  5,  7])}
    data_set_splits[ds_name]=ds[0].node_label_index['n0'].shape[0]+ds[0].node_label_index['n1'].shape[0]
    #这里建议用的node_label_index，但是据我猜测用node_label应该也行
    #对node_label_index属性的介绍见下
  return data_set_splits
dataset = GraphDataset([hete], task='node')
# Splitting the dataset
dataset_train, dataset_val, dataset_test = dataset.split(transductive=True, split_ratio=[0.4, 0.3, 0.3])
datasets = {'train': dataset_train, 'val': dataset_val, 'test': dataset_test}
data_set_splits = compute_dataset_split_counts(datasets)
for dataset_name, num_nodes in data_set_splits.items():
  print("{} dataset has {} nodes".format(dataset_name, num_nodes))

输出：

train dataset has 12 nodes
val dataset has 10 nodes
test dataset has 12 nodes

HeteroGraph.node_label_index: Slicing node label to get the corresponding split G.node_label[G.node_label_index].（出自Introduction — DeepSNAP 0.2.0 documentation）

这写的是个什么玩意儿，这谁看得懂……总之意思就是说可以通过node_label_index来讲数据集划分后的节点通过索引对应到原来的标签，举例来说：

data_train=dataset_train[0]
print(data_train.node_label)
print(data_train.node_label_index)
print(hete.node_label)
print(hete.node_label_index)
print(hete.node_label['n0'][data_train.node_label_index['n0']])

输出：

{'n0': tensor([0, 0, 0, 0, 0, 0]), 'n1': tensor([1, 1, 1, 1, 1, 1])}
{'n0': tensor([ 5, 13, 14,  9,  0,  2]), 'n1': tensor([ 6, 11,  4, 13,  9, 15])}
{'n0': tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]), 'n1': tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])}
{'n0': tensor([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16]), 'n1': tensor([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16])}
tensor([0, 0, 0, 0, 0, 0])

1.8 DeepSNAP数据集可视化

from deepsnap.dataset import GraphDataset
dataset = GraphDataset([hete], task='node')
# Splitting the dataset
dataset_train, dataset_val, dataset_test = dataset.split(transductive=True, split_ratio=[0.4, 0.3, 0.3])
titles = ['Train', 'Validation', 'Test']
for i, dataset in enumerate([dataset_train, dataset_val, dataset_test]):
  n0 = hete._convert_to_graph_index(dataset[0].node_label_index['n0'], 'n0').tolist()
  #[21, 5, 7, 8, 16, 11]
  #看上下文应该是返回该split中node_type为n0的节点的索引。_convert_to_graph_index()返回Tensor
  n1 = hete._convert_to_graph_index(dataset[0].node_label_index['n1'], 'n1').tolist()
  plt.figure(figsize=(7, 7))
  plt.title(titles[i])
  nx.draw(G_orig, pos=pos, node_color="grey", edge_color=colors, labels=labels, font_color='white')
  nx.draw_networkx_nodes(G_orig.subgraph(n0), pos=pos, node_color="blue")
  #subgraph()应该是返回node-induced subgraph的意思，但我找不到对应的文档，算了
  nx.draw_networkx_nodes(G_orig.subgraph(n1), pos=pos, node_color="red")
  show()

2. 异质图节点预测任务

这一部分问题应该是修改自DeepSNAP官方的异质图节点预测任务示例代码：deepsnap/node_classification_acm.py at master · snap-stanford/deepsnap

所以我答案也是从别人写的colab4中抄了一部分，从这个里面抄了一部分（毕竟据我猜测老师出这个题就是照着这个官方答案魔改的）。

首先我们假设有一个图 G GG，其有2种node types a aa 和 b bb，3种three message types m 1 = ( a , r 1 , a ) , m 2 = ( a , r 2 , b ) 和 m 3 = ( a , r 3 , b )。

一个heterogeneous layer要包含3个Heterogeneous GNN layers（本colab中的 HeteroGNNConv），每个 HeteroGNNConv 层只对一种message type做message passing和aggregation。

整体算法流程：

在本colab中，第 l ll 层heterogeneous GNN layer由第 l ll 层Heterogeneous GNN Wrapper layer（即本colab中的 HeteroGNNWrapperConv）进行管理，它直接通过上一层的节点嵌入进行信息传递、聚合到下一层的节点嵌入。

整体算法流程：

2.1 导包

import copy
import torch
import deepsnap
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch_geometric.nn as pyg_nn
from sklearn.metrics import f1_score
from deepsnap.hetero_gnn import forward_op
from deepsnap.hetero_graph import HeteroGraph
from torch_sparse import SparseTensor, matmul

2.2 Heterogeneous GNN Layer

class HeteroGNNConv(pyg_nn.MessagePassing):
    def __init__(self, in_channels_src, in_channels_dst, out_channels):
        super(HeteroGNNConv, self).__init__(aggr="mean")
        self.in_channels_src = in_channels_src
        self.in_channels_dst = in_channels_dst
        self.out_channels = out_channels
        self.lin_dst=nn.Linear(in_channels_dst,out_channels)  #W_d^{(l)[m]}
        self.lin_src=nn.Linear(in_channels_src,out_channels)  #W_s^{(l)[m]}
        self.lin_update=nn.Linear(out_channels*2,out_channels)  #W^{(l)[m]}
    def forward(
        self,
        node_feature_src,
        node_feature_dst,
        edge_index,
        size=None,
        res_n_id=None,
    ):
        return self.propagate(edge_index,size=size,
               node_feature_src=node_feature_src,
               node_feature_dst=node_feature_dst,res_n_id=res_n_id)
    def message_and_aggregate(self, edge_index, node_feature_src):
        # Here edge_index is torch_sparse SparseTensor.
        out=matmul(edge_index,node_feature_src,reduce=self.aggr)
        #实不相瞒，我没看懂，但是算了，以后再说吧
        return out
    def update(self, aggr_out, node_feature_dst, res_n_id):
        aggr_out=self.lin_src(aggr_out)
        node_feature_dst=self.lin_dst(node_feature_dst)
        concat_features = torch.cat((node_feature_dst, aggr_out),dim=-1)
        #维度-1在这里就是维度1
        aggr_out = self.lin_update(concat_features)
        return aggr_out

2.3 Heterogeneous GNN Wrapper Layer

在对每一种message type应用GNN层（HeteroGNNConv）时，我们需要在每一层上将它们聚合起来。

在本colab中将应用两种聚合方式。

第一种：mean

节点 v 的node type是 d，M 是destination node的node type是 d dd 的message type的数量。

第二种：semantic level attention introduced in HAN (Wang et al. (2019))

m 是message type，d 是destination node type。

class HeteroGNNWrapperConv(deepsnap.hetero_gnn.HeteroConv):
    #文档：https://snap.stanford.edu/deepsnap/modules/hetero_gnn.html
    def __init__(self, convs, args, aggr="mean"):
        super(HeteroGNNWrapperConv, self).__init__(convs, None)
        self.aggr = aggr
        # Map the index and message type
        self.mapping = {}
        # A numpy array that stores the final attention probability
        self.alpha = None
        self.attn_proj = None
        if self.aggr == "attn":
            self.attn_proj = nn.Sequential(
                nn.Linear(args['hidden_size'], args['attn_size']),
                nn.Tanh(),
                nn.Linear(args['attn_size'], 1, bias=False),
            )
    def reset_parameters(self):
        super(HeteroConvWrapper, self).reset_parameters()
        if self.aggr == "attn":
            for layer in self.attn_proj.children():
                layer.reset_parameters()
    def forward(self, node_features, edge_indices):
      #edge_indices: 字典，key是message type，value是对应的edge_index Tensor
        message_type_emb = {}
        for message_key, message_type in edge_indices.items():
            src_type, edge_type, dst_type = message_key
            node_feature_src = node_features[src_type]
            node_feature_dst = node_features[dst_type]
            edge_index = edge_indices[message_key]
            message_type_emb[message_key] = (
                self.convs[message_key](
                    node_feature_src,
                    node_feature_dst,
                    edge_index,
                )
            )
        node_emb = {dst: [] for _, _, dst in message_type_emb.keys()}
        mapping = {}        
        for (src, edge_type, dst), item in message_type_emb.items():
            mapping[len(node_emb[dst])] = (src, edge_type, dst)
            node_emb[dst].append(item)
        #mapping示例: {0: ('paper', 'author', 'paper'), 1: ('paper', 'subject', 'paper')}
        self.mapping = mapping
        for node_type, embs in node_emb.items():
            if len(embs) == 1:
                node_emb[node_type] = embs[0]
            else:
                node_emb[node_type] = self.aggregate(embs)
        return node_emb
    def aggregate(self, xs):
        #xs是Tensor（message type的embeddings）的list
        if self.aggr == "mean":
            x = torch.stack(xs, dim=-1)
            return x.mean(dim=-1)
        elif self.aggr == "attn":
            N = xs[0].shape[0] # Number of nodes for that node type
            M = len(xs) # Number of message types for that node type
            x = torch.cat(xs, dim=0).view(M, N, -1) # M * N * D
            z = self.attn_proj(x).view(M, N) # M * N * 1
            z = z.mean(1) # M * 1
            alpha = torch.softmax(z, dim=0) # M * 1
            # Store the attention result to self.alpha as np array
            self.alpha = alpha.view(-1).data.cpu().numpy()
            #(len(xs),)
            #self.alpha不用于反向传播等操作，仅用于看不同层对不同message type的attention值
            alpha = alpha.view(M, 1, 1)
            x = x * alpha
            return x.sum(dim=0)

2.4 初始化Heterogeneous GNN Layers

def generate_convs(hetero_graph, conv, hidden_size, first_layer=False):
    """
    入参：
    hetero_graph：DeepSNAP `HeteroGraph` object
    conv: HeteroGNNConv
    第一层：输入维度为特征维度，输出维度为隐藏层维度
    非第一层：输入维度为隐藏层维度，输出维度也是隐藏层维度
  返回值：一个 `HeteroGNNConv` 层的字典，key是message types。
    """
    convs = {}
    for message_type in hetero_graph.message_types:
        if first_layer is True:
            src_type = message_type[0]
            dst_type = message_type[2]
            src_size = hetero_graph.num_node_features(src_type)
            dst_size = hetero_graph.num_node_features(dst_type)
            convs[message_type] = conv(src_size,dst_size, hidden_size)
        else:
            convs[message_type] = conv(hidden_size, hidden_size, hidden_size)
    return convs

注意这里推荐使用 deepsnap.hetero_graph.HeteroGraph.num_node_features(node_type) 方法，但是经我测试在question 1中建立的异质图 hete 上运行 hete.num_node_features('n1') 会报错：AttributeError: 'list' object has no attribute 'shape'

这应该是因为 hete 上的特征是list格式而非Tensor格式，我觉得这是DeepSNAP尚有不足之处。

2.5 HeteroGNN

我们建立一个包含2层 HeteroGNNWrapperConv 的HeteroGNN模型。

self.convs1 → self.bns1 → self.relus1 → self.convs2 → self.bns2 → self.relus2 → self.post_mps

class HeteroGNN(torch.nn.Module):
    def __init__(self, hetero_graph, args, aggr="mean"):
        super(HeteroGNN, self).__init__()
        self.aggr = aggr
        self.hidden_size = args['hidden_size']
        self.bns1 = nn.ModuleDict()
        self.bns2 = nn.ModuleDict()
        self.relus1 = nn.ModuleDict()
        self.relus2 = nn.ModuleDict()
        self.post_mps = nn.ModuleDict()
        convs1 = generate_convs(hetero_graph, HeteroGNNConv, self.hidden_size, first_layer=True)
        convs2 = generate_convs(hetero_graph, HeteroGNNConv, self.hidden_size)
        self.convs1 = HeteroGNNWrapperConv(convs1, args, aggr=self.aggr)
        self.convs2 = HeteroGNNWrapperConv(convs2, args, aggr=self.aggr)
        for node_type in hetero_graph.node_types:
            self.bns1[node_type] = torch.nn.BatchNorm1d(self.hidden_size, eps=1)
            self.bns2[node_type] = torch.nn.BatchNorm1d(self.hidden_size, eps=1)
            self.post_mps[node_type] = nn.Linear(self.hidden_size, hetero_graph.num_node_labels(node_type))
            self.relus1[node_type] = nn.LeakyReLU()
            self.relus2[node_type] = nn.LeakyReLU()
    def forward(self, node_feature, edge_index):
        #node_feature是一个字典，key是node types，values是对应的feature Tensors
        #edge_index也是一个字典，字典，key是message types，value是对应的edge_index Tensor
        x = node_feature
        x = self.convs1(x, edge_index)
        x = forward_op(x, self.bns1)  #这个方法介绍见下
        x = forward_op(x, self.relus1)
        x = self.convs2(x, edge_index)
        x = forward_op(x, self.bns2)
        x = forward_op(x, self.relus2)
        x = forward_op(x, self.post_mps)
        return x
    def loss(self, preds, y, indices):
        loss = 0
        loss_func = F.cross_entropy
        for node_type in preds:
            idx = indices[node_type]
            loss += loss_func(preds[node_type][idx], y[node_type][idx])
        return loss

forward_op(x, module_dict, **kwargs)：

文档：deepsnap.hetero_gnn.forward_op

大意来说就是给定如代码所示格式的 x 和 module_dict 参数，forward_op() 方法会按照二者对应的key来对应地按照给定的参数将 x 的value运行在 module_dict 的value上。

2.6 构建 train() 和 test() 函数

def train(model, optimizer, hetero_graph, train_idx):
    model.train()
    optimizer.zero_grad()
    preds = model(hetero_graph.node_feature, hetero_graph.edge_index)
    loss = model.loss(preds, hetero_graph.node_label, train_idx)
    loss.backward()
    optimizer.step()
    return loss.item()
def test(model, graph, indices, best_model=None, best_val=0):
    model.eval()
    accs = []
    for index in indices:
        preds = model(graph.node_feature, graph.edge_index)
        num_node_types = 0
        micro = 0
        macro = 0
        for node_type in preds:
            idx = index[node_type]
            pred = preds[node_type][idx]
            pred = pred.max(1)[1]
            label_np = graph.node_label[node_type][idx].cpu().numpy()
            pred_np = pred.cpu().numpy()
            micro = f1_score(label_np, pred_np, average='micro')
            macro = f1_score(label_np, pred_np, average='macro')
            num_node_types += 1
        #注意这里，实际上对F1 score求平均是没有意义的
        #但是在我们的例子中其实只有一种node type所以也无所谓了……
        micro /= num_node_types
        macro /= num_node_types
        accs.append((micro, macro))
    if accs[1][0] > best_val:
        best_val = accs[1][0]
        best_model = copy.deepcopy(model)
        #注意这里要深拷贝！我就被这个深拷贝浅拷贝坑过！
        #反正先记住这里要深拷贝好了，以后我还准备专门写博文讲一下这个深拷贝浅拷贝直接引用的事
    return accs, best_model, best_val

2.7 设置超参

args = {
    'device': torch.device('cuda' if torch.cuda.is_available() else 'cpu'),
    'hidden_size': 64,
    'epochs': 100,
    'weight_decay': 1e-5,
    'lr': 0.003,
    'attn_size': 32,
}

2.8 数据集导入及预处理

在这一环节中我们将使用Tensor backend而非NetworkX backend了。

在本colab中使用的 ACM(3025) 数据集来源于 HAN (Wang et al. (2019))，本colab的数据集提取自DGL的ACM.mat。

原始的ACM数据集有3种node types和2种edge (relation) types。为简化起见，我们将其简化为1种node type和2种edge types：

所以在我们的数据集中，只有一种node type (paper) 和2种message types (paper, author, paper) and (paper, subject, paper)

数据集下载方式见我的GitHub项目的README文件2021/6/21更新部分：PolarisRisingWar/cs224w-2021-winter-colab: cs224w（图机器学习）2021冬季课程的colab

print("Device: {}".format(args['device']))
# Load the data
data = torch.load("acm.pkl")
#data是一个字典，key是str，value是Tensor
# Message types
message_type_1 = ("paper", "author", "paper")
message_type_2 = ("paper", "subject", "paper")
# Dictionary of edge indices
edge_index = {}
edge_index[message_type_1] = data['pap']
edge_index[message_type_2] = data['psp']
# Dictionary of node features
node_feature = {}
node_feature["paper"] = data['feature']
# Dictionary of node labels
node_label = {}
node_label["paper"] = data['label']
# Load the train, validation and test indices
train_idx = {"paper": data['train_idx'].to(args['device'])}
val_idx = {"paper": data['val_idx'].to(args['device'])}
test_idx = {"paper": data['test_idx'].to(args['device'])}
# Construct a deepsnap tensor backend HeteroGraph
hetero_graph = HeteroGraph(
    node_feature=node_feature,
    node_label=node_label,
    edge_index=edge_index,
    directed=True
)
print(f"ACM heterogeneous graph: {hetero_graph.num_nodes()} nodes, {hetero_graph.num_edges()} edges")
# Node feature and node label to device
for key in hetero_graph.node_feature:
    hetero_graph.node_feature[key] = hetero_graph.node_feature[key].to(args['device'])
for key in hetero_graph.node_label:
    hetero_graph.node_label[key] = hetero_graph.node_label[key].to(args['device'])
# Edge_index to sparse tensor and to device
for key in hetero_graph.edge_index:
    edge_index = hetero_graph.edge_index[key]
    adj = SparseTensor(row=edge_index[0], col=edge_index[1], sparse_sizes=(hetero_graph.num_nodes('paper'), hetero_graph.num_nodes('paper')))
    hetero_graph.edge_index[key] = adj.t().to(args['device'])
print(hetero_graph.edge_index[message_type_1])
print(hetero_graph.edge_index[message_type_2])

输出内容：

Device: cuda
ACM heterogeneous graph: {'paper': 3025} nodes, {('paper', 'author', 'paper'): 26256, ('paper', 'subject', 'paper'): 2207736} edges
SparseTensor(row=tensor([   0,    0,    0,  ..., 3024, 3024, 3024], device='cuda:0'),
             col=tensor([   8,   20,   51,  ..., 2948, 2983, 2991], device='cuda:0'),
             size=(3025, 3025), nnz=26256, density=0.29%)
SparseTensor(row=tensor([   0,    0,    0,  ..., 3024, 3024, 3024], device='cuda:0'),
             col=tensor([  75,  434,  534,  ..., 3020, 3021, 3022], device='cuda:0'),
             size=(3025, 3025), nnz=2207736, density=24.13%)

2.9 Training the Mean Aggregation

best_model = None
best_val = 0
model = HeteroGNN(hetero_graph, args, aggr="mean").to(args['device'])
optimizer = torch.optim.Adam(model.parameters(), lr=args['lr'], weight_decay=args['weight_decay'])
for epoch in range(args['epochs']):
    loss = train(model, optimizer, hetero_graph, train_idx)
    accs, best_model, best_val = test(model, hetero_graph, [train_idx, val_idx, test_idx], best_model, best_val)
    print(
        f"Epoch {epoch + 1}: loss {round(loss, 5)}, "
        f"train micro {round(accs[0][0] * 100, 2)}%, train macro {round(accs[0][1] * 100, 2)}%, "
        f"valid micro {round(accs[1][0] * 100, 2)}%, valid macro {round(accs[1][1] * 100, 2)}%, "
        f"test micro {round(accs[2][0] * 100, 2)}%, test macro {round(accs[2][1] * 100, 2)}%"
    )
best_accs, _, _ = test(best_model, hetero_graph, [train_idx, val_idx, test_idx])
print(
    f"Best model: "
    f"train micro {round(best_accs[0][0] * 100, 2)}%, train macro {round(best_accs[0][1] * 100, 2)}%, "
    f"valid micro {round(best_accs[1][0] * 100, 2)}%, valid macro {round(best_accs[1][1] * 100, 2)}%, "
    f"test micro {round(best_accs[2][0] * 100, 2)}%, test macro {round(best_accs[2][1] * 100, 2)}%"
)

每一轮的输出略，最好模型的输出：

Best model: train micro 99.83%, train macro 99.83%, valid micro 98.33%, valid macro 98.33%, test micro 87.86%, test macro 87.78%

2.10 Training the Attention Aggregation

best_model = None
best_val = 0
output_size = hetero_graph.num_node_labels('paper')
model = HeteroGNN(hetero_graph, args, aggr="attn").to(args['device'])
optimizer = torch.optim.Adam(model.parameters(), lr=args['lr'], weight_decay=args['weight_decay'])
for epoch in range(args['epochs']):
    loss = train(model, optimizer, hetero_graph, train_idx)
    accs, best_model, best_val = test(model, hetero_graph, [train_idx, val_idx, test_idx], best_model, best_val)
    print(
        f"Epoch {epoch + 1}: loss {round(loss, 5)}, "
        f"train micro {round(accs[0][0] * 100, 2)}%, train macro {round(accs[0][1] * 100, 2)}%, "
        f"valid micro {round(accs[1][0] * 100, 2)}%, valid macro {round(accs[1][1] * 100, 2)}%, "
        f"test micro {round(accs[2][0] * 100, 2)}%, test macro {round(accs[2][1] * 100, 2)}%"
    )
best_accs, _, _ = test(best_model, hetero_graph, [train_idx, val_idx, test_idx])
print(
    f"Best model: "
    f"train micro {round(best_accs[0][0] * 100, 2)}%, train macro {round(best_accs[0][1] * 100, 2)}%, "
    f"valid micro {round(best_accs[1][0] * 100, 2)}%, valid macro {round(best_accs[1][1] * 100, 2)}%, "
    f"test micro {round(best_accs[2][0] * 100, 2)}%, test macro {round(best_accs[2][1] * 100, 2)}%"
)

每一轮的输出略，最好模型的输出：

Best model: train micro 99.67%, train macro 99.67%, valid micro 97.67%, valid macro 97.66%, test micro 85.79%, test macro 85.27%

2.11 Attention for each Message Type

if model.convs1.alpha is not None and model.convs2.alpha is not None:
    for idx, message_type in model.convs1.mapping.items():
        print(f"Layer 1 has attention {model.convs1.alpha[idx]} on message type {message_type}")
    for idx, message_type in model.convs2.mapping.items():
        print(f"Layer 2 has attention {model.convs2.alpha[idx]} on message type {message_type}")

输出：

Layer 1 has attention 0.960588812828064 on message type ('paper', 'author', 'paper')
Layer 1 has attention 0.03941113129258156 on message type ('paper', 'subject', 'paper')
Layer 2 has attention 0.30975428223609924 on message type ('paper', 'author', 'paper')
Layer 2 has attention 0.6902456879615784 on message type ('paper', 'subject', 'pape

cs224w（图机器学习）2021冬季课程学习笔记18 Colab 4：异质图

Question 1. DeepSNAP异质图简介

1.1 Question 1.1：分配Node Type and Node Features

1.2 Question 1.2：分配Edge Types

1.3 NetworkX异质图可视化

1.4 将NetworkX异质图转换为DeepSNAP异质图

1.5 Question1.3：每一node type有多少个节点

1.6 Question 1.4：每一message type有多少条边

1.7 Question 1.5：数据集划分：每一个split中有多少个节点？

1.8 DeepSNAP数据集可视化

2. 异质图节点预测任务

2.1 导包

2.2 Heterogeneous GNN Layer

2.3 Heterogeneous GNN Wrapper Layer

2.4 初始化Heterogeneous GNN Layers

2.5 HeteroGNN

2.6 构建 train() 和 test() 函数

2.7 设置超参

2.8 数据集导入及预处理

2.9 Training the Mean Aggregation

2.10 Training the Attention Aggregation

2.11 Attention for each Message Type

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cs224w（图机器学习）2021冬季课程学习笔记18 Colab 4：异质图

Question 1. DeepSNAP异质图简介

1.1 Question 1.1：分配Node Type and Node Features

1.2 Question 1.2：分配Edge Types

1.3 NetworkX异质图可视化

1.4 将NetworkX异质图转换为DeepSNAP异质图

1.5 Question1.3：每一node type有多少个节点

1.6 Question 1.4：每一message type有多少条边

1.7 Question 1.5：数据集划分：每一个split中有多少个节点？

1.8 DeepSNAP数据集可视化

2. 异质图节点预测任务

2.1 导包

2.2 Heterogeneous GNN Layer

2.3 Heterogeneous GNN Wrapper Layer

2.4 初始化Heterogeneous GNN Layers

2.5 HeteroGNN

2.6 构建 train() 和 test() 函数

2.7 设置超参

2.8 数据集导入及预处理

2.9 Training the Mean Aggregation

2.10 Training the Attention Aggregation

2.11 Attention for each Message Type

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