使用StellarGraph创建嵌入是不可复制的

Question

我正在使用StellarGraph (一个奇妙的图形神经网络包)，并试图为特定的图/特征集创建嵌入。不幸的是，每次创建/训练图形时，嵌入都是不同的，尽管每次都提供相同的信息。

是这个错误，还是我不正确地使用StellarGraph？

下面是说明这一问题的代码：

import networkx as nx
import random
import numpy as np
import pandas as pd
import keras
import stellargraph as sg
from stellargraph.mapper import GraphSAGELinkGenerator, GraphSAGENodeGenerator
from stellargraph.layer import GraphSAGE, link_classification
from stellargraph.data import UnsupervisedSampler

# Establish random seed
RANDOM_SEED = 42
random.seed(RANDOM_SEED)

# Create a graph from well-known karate club data
print(f"Creating graph")
graph = nx.karate_club_graph()

# Create features for each node
print(f"Creating features")
features = []
nodes = list(graph.nodes)
columns = ["c-" + str(x) for x in range(10)]
nodes.sort()
for node in nodes:
    f = {c: random.random() for c in columns}
    features.append(f)

features_df = pd.DataFrame(features)
print(f"features_df: \n{features_df}")

for i in range(2):
    print(f"----- Iteration: {i} -----")

    # Create the model and generators
    print(f"Creating the model and generators")
    Gs = sg.StellarGraph(graph, node_features=features_df)
    unsupervisedSamples = UnsupervisedSampler(Gs, nodes=graph.nodes(), length=5, number_of_walks=3, seed=RANDOM_SEED)
    train_gen = GraphSAGELinkGenerator(Gs, 50, [5, 5], seed=RANDOM_SEED).flow(unsupervisedSamples)
    graphsage = GraphSAGE(layer_sizes=[100, 100], generator=train_gen, bias=True, dropout=0.0, normalize="l2")
    x_inp_src, x_out_src = graphsage.node_model(flatten_output=False)
    x_inp_dst, x_out_dst = graphsage.node_model(flatten_output=False)

    x_inp = [x for ab in zip(x_inp_src, x_inp_dst) for x in ab]
    x_out = [x_out_src, x_out_dst]
    edge_embedding_method = "l2"
    prediction = link_classification(output_dim=1, output_act="sigmoid", edge_embedding_method=edge_embedding_method)(x_out)

    # Create and train the Keras model
    model = keras.Model(inputs=x_inp, outputs=prediction)
    learning_rate = 1e-2
    model.compile(
        optimizer=keras.optimizers.Adam(lr=learning_rate),
        loss=keras.losses.binary_crossentropy,
        metrics=[keras.metrics.binary_accuracy])

    _ = model.fit_generator(train_gen, epochs=5, verbose=2, use_multiprocessing=False, workers=1, shuffle=False)

    # Create the embeddings
    print(f"Creating the embeddings")
    nodes = list(graph.nodes)
    nodes.sort()
    print(f"Nodes: {nodes}")

    # Create a generator that serves up nodes for use in embedding prediction / creation
    node_gen = GraphSAGENodeGenerator(Gs, 50, [5, 5], seed=RANDOM_SEED).flow(nodes)

    embedding_model = keras.Model(inputs=x_inp_src, outputs=x_out_src)
    embeddings = embedding_model.predict_generator(node_gen, workers=4, verbose=1)
    embeddings = embeddings[:, 0, :]

    np.set_printoptions(threshold=10)
    print(f"embeddings: {embeddings.shape} \n{embeddings}")

在执行代码时，有许多调试(打印输出)语句。(输出示例如下所示)注意，尽管有相同的输入、图形配置、模型配置和随机值，但是嵌入是不同的。

----- Iteration: 0 -----
:
:
1/1 [==============================] - 0s 58ms/step
embeddings: (34, 100) 
[[-0.10566715  0.02253576 -0.18743701 ... -0.1028127   0.03689012
  -0.02482301]
 [-0.03171733  0.01606975 -0.08616363 ... -0.11775644  0.0429472
  -0.02371055]
 [-0.05802531  0.03910012 -0.10229243 ... -0.15050544  0.06637941
  -0.01950052]
 ...
 [ 0.03011296  0.08852117 -0.01836969 ... -0.154132    0.03844732
  -0.08643046]
 [ 0.01052345 -0.0123206   0.08913474 ... -0.11741614  0.03202919
  -0.04432516]
 [ 0.01951274  0.06263477  0.07959272 ... -0.10350229  0.05735112
  -0.0368157 ]]
:
:
----- Iteration: 1 -----
embeddings: (34, 100) 
[[ 0.11182436 -0.02642134  0.01168384 ...  0.10322241 -0.01680471
  -0.03918815]
 [ 0.02391489  0.02674667 -0.00091334 ...  0.12946768 -0.02389602
  -0.01414653]
 [ 0.08718258 -0.01711811 -0.05704292 ...  0.13477756 -0.00658288
  -0.05889895]
 ...
 [ 0.06843725 -0.13134597 -0.10870655 ...  0.11091235 -0.05146989
  -0.06138216]
 [-0.00593233 -0.05901312 -0.02113489 ... -0.01590953 -0.02516254
  -0.02280537]
 [ 0.00871993 -0.04059998 -0.07237951 ... -0.01590569 -0.00954109
  -0.01116194]]

Answer 1

这以前是stellargraph中的一个bug，现在已经在v0.9.0 https://github.com/stellargraph/stellargraph/releases/tag/v0.9.0中解决了。

无监督的GraphSAGE现在已经更新和测试的重现性。确保所有种子都已设置，运行相同的管道应该提供可重复的嵌入。

目前，“确保所有种子都被设置”用于无监督的GraphSAGE意味着：

在构造random

• providing和对象时，
  • 修复了这些外部包的种子：numpy、tensorflow和numpy。这些类用于执行随机游动和邻里抽样。