message_passing.py

import tensorflow as tf


def message_passing(nodes, edges, edge_features, message_fn, edge_keep_prob=1.0):
    """
    Pass messages between nodes and sum the incoming messages at each node.
    Implements equation 1 and 2 in the paper, i.e. m_{.j}^t &= \sum_{i \in N(j)} f(h_i^{t-1}, h_j^{t-1})

    :param nodes: (n_nodes, n_features) tensor of node hidden states.
    :param edges: (n_edges, 2) tensor of indices (i, j) indicating an edge from nodes[i] to nodes[j].
    :param edge_features: features for each edge. Set to zero if the edges don't have features.
    :param message_fn: message function, will be called with input of shape (n_edges, 2*n_features + edge_features). The output shape is (n_edges, n_outputs), where you decide the size of n_outputs
    :param edge_keep_prob: The probability by which edges are kept. Basically dropout for edges. Not used in the paper.
    :return: (n_nodes, n_output) Sum of messages arriving at each node.
    """
    n_nodes = tf.shape(nodes)[0]
    n_features = nodes.get_shape()[1].value
    n_edges = tf.shape(edges)[0]

    message_inputs = tf.gather(nodes, edges)  # n_edges, 2, n_features
    reshaped = tf.concat([tf.reshape(message_inputs, (-1, 2 * n_features)), edge_features], 1)
    messages = message_fn(reshaped)  # n_edges, n_output
    messages = tf.nn.dropout(messages, edge_keep_prob, noise_shape=(n_edges, 1))

    n_output = messages.get_shape()[1].value

    idx_i, idx_j = tf.split(edges, 2, 1)
    out_shape = (n_nodes, n_output)
    updates = tf.scatter_nd(idx_j, messages, out_shape)

    return updates