图神经网络（GNN）TensorFlow实现

图神经网络的研究与图嵌入或网络嵌入密切相关，图嵌入或网络嵌入是数据挖掘和机器学习界日益关注的另一个课题。图嵌入旨在通过保留图的网络拓扑结构和节点内容信息，将图中顶点表示为低维向量，以便使用简单的机器学习算法（例如，支持向量机分类）进行处理。许多图嵌入算法通常是无监督的算法，它们可以大致可以划分为三个类别，即矩阵分解、随机游走和深度学习方法。同时图嵌入的深度学习方法也属于图神经网络，包括基于图自动编码器的算法（如DNGR和SDNE）和无监督训练的图卷积神经网络（如GraphSage）。——https://zhuanlan.zhihu.com/p/75307407

下面给出一个图神经网络TensorFlow的实现，代码来自https://github.com/Ivan0131/gnn_demo。

import tensorflow as tf
import numpy as np
import gnn.gnn_utils as gnn_utils

data_path = "./data"
set_name = "sub_15_7_200"

# 训练集
inp, arcnode, nodegraph, nodein, labels, _ = gnn_utils.set_load_general(data_path, "train", set_name=set_name)
inp = [a[:, 1:] for a in inp]

# 验证集
inp_val, arcnode_val, nodegraph_val, nodein_val, labels_val, _ = gnn_utils.set_load_general(data_path, "validation",
                                                                                            set_name=set_name)
inp_val = [a[:, 1:] for a in inp_val]
input_dim = len(inp[0][0])
state_dim = 10
output_dim = 2
state_threshold = 0.001
max_iter = 50

tf.compat.v1.disable_eager_execution()
tf.reset_default_graph()
comp_inp = tf.placeholder(tf.float32, shape=(None, input_dim), name="input")
y = tf.placeholder(tf.float32, shape=(None, output_dim), name="target")

state = tf.placeholder(tf.float32, shape=(None, state_dim), name="state")
state_old = tf.placeholder(tf.float32, shape=(None, state_dim), name="old_state")

ArcNode = tf.sparse_placeholder(tf.float32, name="ArcNode")


def f_w(inp):
    with tf.variable_scope('State_net'):
        layer1 = tf.layers.dense(inp, 5, activation=tf.nn.sigmoid)
        layer2 = tf.layers.dense(layer1, state_dim, activation=tf.nn.sigmoid)
        return layer2


def g_w(inp):
    with tf.variable_scope('Output_net'):
        layer1 = tf.layers.dense(inp, 5, activation=tf.nn.sigmoid)
        layer2 = tf.layers.dense(layer1, output_dim, activation=None)
        return layer2


def convergence(a, state, old_state, k):
    with tf.variable_scope('Convergence'):
        # assign current state to old state
        old_state = state

        # 获取子结点上一个时刻的状态
        # grub states of neighboring node
        gat = tf.gather(old_state, tf.cast(a[:, 0], tf.int32))
        print(a[:, 0])

        # 去除第一列，即子结点的id
        # slice to consider only label of the node and that of it's neighbor
        # sl = tf.slice(a, [0, 1], [tf.shape(a)[0], tf.shape(a)[1] - 1])
        # equivalent code
        sl = a[:, 1:]

        # 将子结点上一个时刻的状态放到最后一列
        # concat with retrieved state
        inp = tf.concat([sl, gat], axis=1)
        print('inp', inp)

        # evaluate next state and multiply by the arch-node conversion matrix to obtain per-node states
        # 计算子结点对父结点状态的贡献
        layer1 = f_w(inp)
        # 聚合子结点对父结点状态的贡献，得到当前时刻的父结点的状态
        print('ArcNode', ArcNode)
        state = tf.sparse_tensor_dense_matmul(ArcNode, layer1)

        # update the iteration counter
        k = k + 1
    return a, state, old_state, k


def condition(a, state, old_state, k):
    # evaluate condition on the convergence of the state
    with tf.variable_scope('condition'):
        # 检查当前状态和上一个时刻的状态的欧式距离是否小于阈值
        # evaluate distance by state(t) and state(t-1)
        outDistance = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(state, old_state)), 1) + 1e-10)
        # vector showing item converged or not (given a certain threshold)
        checkDistanceVec = tf.greater(outDistance, state_threshold)
        print(outDistance)
        print(checkDistanceVec)
        c1 = tf.reduce_any(checkDistanceVec)
        print(c1)
        # 是否达到最大迭代次数
        c2 = tf.less(k, max_iter)
        print(c2)
    return tf.logical_and(c1, c2)


# 迭代计算，直到状态达到稳定状态
with tf.variable_scope('Loop'):
    k = tf.constant(0)
    res, st, old_st, num = tf.while_loop(condition, convergence, [comp_inp, state, state_old, k])
    out = g_w(st)

loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(labels=y, logits=out))
accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(out, 1), tf.argmax(y, 1)), dtype=tf.float32))
optimizer = tf.train.AdamOptimizer(0.001)
grads = optimizer.compute_gradients(loss)
train_op = optimizer.apply_gradients(grads, name='train_op')



# 模型训练
num_epoch = 5000
# 训练集placeholder输入
arcnode_train = tf.SparseTensorValue(indices=arcnode[0].indices, values=arcnode[0].values,
                                     dense_shape=arcnode[0].dense_shape)
fd_train = {comp_inp: inp[0], state: np.zeros((arcnode[0].dense_shape[0], state_dim)),
            state_old: np.ones((arcnode[0].dense_shape[0], state_dim)),
            ArcNode: arcnode_train, y: labels}
# 验证集placeholder输入
arcnode_valid = tf.SparseTensorValue(indices=arcnode_val[0].indices, values=arcnode_val[0].values,
                                     dense_shape=arcnode_val[0].dense_shape)
fd_valid = {comp_inp: inp_val[0], state: np.zeros((arcnode_val[0].dense_shape[0], state_dim)),
            state_old: np.ones((arcnode_val[0].dense_shape[0], state_dim)),
            ArcNode: arcnode_valid, y: labels_val}

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    sess.run(tf.local_variables_initializer())
    for i in range(0, num_epoch):
        _, loss_val, accuracy_val = sess.run(
            [train_op, loss, accuracy],
            feed_dict=fd_train)
        if i % 100 == 0:
            loss_valid_val, accuracy_valid_val = sess.run(
                [loss, accuracy],
                feed_dict=fd_valid)
            print(
                "iter %s\t training loss: %s,\t training accuracy: %s,\t validation loss: %s,\t validation accuracy: %s" %
                (i, loss_val, accuracy_val, loss_valid_val, accuracy_valid_val))

数据集和参考代码：https://github.com/Ivan0131/gnn_demo