代码实现：
通过一维卷积增加非线性，赋予不同权重，然后通过矩阵加矩阵的转置，来对attention的权重进行学习，通过-1e9(1-邻接矩阵)，以及softmax来过滤掉不与当前顶点相连的节点。通过叠加GAT网络，来对高阶节点信息进行加权。。。

import numpy as np
import tensorflow as tf

from utils import layers
from models.base_gattn import BaseGAttN

class GAT(BaseGAttN):
    conv1d = tf.layers.conv1d

def attn_head(seq, out_sz, bias_mat, activation, in_drop=0.0, coef_drop=0.0, residual=False):
    with tf.name_scope('my_attn'):
        if in_drop != 0.0:
            seq = tf.nn.dropout(seq, 1.0 - in_drop)

        seq_fts = tf.layers.conv1d(seq, out_sz, 1, use_bias=False)

        # simplest self-attention possible
        f_1 = tf.layers.conv1d(seq_fts, 1, 1)
        f_2 = tf.layers.conv1d(seq_fts, 1, 1)
        logits = f_1 + tf.transpose(f_2, [0, 2, 1])
        coefs = tf.nn.softmax(tf.nn.leaky_relu(logits) + bias_mat)

        if coef_drop != 0.0:
            coefs = tf.nn.dropout(coefs, 1.0 - coef_drop)
        if in_drop != 0.0:
            seq_fts = tf.nn.dropout(seq_fts, 1.0 - in_drop)

        vals = tf.matmul(coefs, seq_fts)
        ret = tf.contrib.layers.bias_add(vals)

        # residual connection
        if residual:
            if seq.shape[-1] != ret.shape[-1]:
                ret = ret + conv1d(seq, ret.shape[-1], 1) # activation
            else:
                ret = ret + seq

        return activation(ret)  # activation
    def inference(inputs, nb_classes, nb_nodes, training, attn_drop, ffd_drop,
            bias_mat, hid_units, n_heads, activation=tf.nn.elu, residual=False):
        attns = []
        for _ in range(n_heads[0]):
            attns.append(layers.attn_head(inputs, bias_mat=bias_mat,
                out_sz=hid_units[0], activation=activation,
                in_drop=ffd_drop, coef_drop=attn_drop, residual=False))
        h_1 = tf.concat(attns, axis=-1)
        for i in range(1, len(hid_units)):
            h_old = h_1
            attns = []
            for _ in range(n_heads[i]):
                attns.append(layers.attn_head(h_1, bias_mat=bias_mat,
                    out_sz=hid_units[i], activation=activation,
                    in_drop=ffd_drop, coef_drop=attn_drop, residual=residual))
            h_1 = tf.concat(attns, axis=-1)
        out = []
        for i in range(n_heads[-1]):
            out.append(layers.attn_head(h_1, bias_mat=bias_mat,
                out_sz=nb_classes, activation=lambda x: x,
                in_drop=ffd_drop, coef_drop=attn_drop, residual=False))
        logits = tf.add_n(out) / n_heads[-1]
    
        return logits