Source code for dgl.nn.mxnet.conv.densechebconv

"""MXNet Module for DenseChebConv"""
# pylint: disable= no-member, arguments-differ, invalid-name
import math

import mxnet as mx
from mxnet import nd
from mxnet.gluon import nn

[docs]class DenseChebConv(nn.Block): r"""Chebyshev Spectral Graph Convolution layer from `Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering <>`__ We recommend to use this module when applying ChebConv on dense graphs. Parameters ---------- in_feats: int Dimension of input features :math:`h_i^{(l)}`. out_feats: int Dimension of output features :math:`h_i^{(l+1)}`. k : int Chebyshev filter size. activation : function, optional Activation function, default is ReLu. bias : bool, optional If True, adds a learnable bias to the output. Default: ``True``. See also -------- `ChebConv <>`__ """ def __init__(self, in_feats, out_feats, k, bias=True): super(DenseChebConv, self).__init__() self._in_feats = in_feats self._out_feats = out_feats self._k = k with self.name_scope(): self.fc = nn.Sequential() for _ in range(k): self.fc.add( nn.Dense( out_feats, in_units=in_feats, use_bias=False, weight_initializer=mx.init.Xavier( magnitude=math.sqrt(2.0) ), ) ) if bias: self.bias = self.params.get( "bias", shape=(out_feats,), init=mx.init.Zero() ) else: self.bias = None
[docs] def forward(self, adj, feat, lambda_max=None): r""" Description ----------- Compute (Dense) Chebyshev Spectral Graph Convolution layer. Parameters ---------- adj : mxnet.NDArray The adjacency matrix of the graph to apply Graph Convolution on, should be of shape :math:`(N, N)`, where a row represents the destination and a column represents the source. feat : mxnet.NDArray The input feature of shape :math:`(N, D_{in})` where :math:`D_{in}` is size of input feature, :math:`N` is the number of nodes. lambda_max : float or None, optional A float value indicates the largest eigenvalue of given graph. Default: None. Returns ------- mxnet.NDArray The output feature of shape :math:`(N, D_{out})` where :math:`D_{out}` is size of output feature. """ A = adj.astype(feat.dtype).as_in_context(feat.context) num_nodes = A.shape[0] in_degree = 1.0 / nd.clip(A.sum(axis=1), 1, float("inf")).sqrt() D_invsqrt = nd.diag(in_degree) I = nd.eye(num_nodes, ctx=A.context) L = I -,, D_invsqrt)) if lambda_max is None: # NOTE(zihao): this only works for directed graph. lambda_max = (nd.linalg.syevd(L)[1]).max() L_hat = 2 * L / lambda_max - I Z = [nd.eye(num_nodes, ctx=A.context)] Zh = self.fc[0](feat) for i in range(1, self._k): if i == 1: Z.append(L_hat) else: Z.append(2 *, Z[-1]) - Z[-2]) Zh = Zh +[i], self.fc[i](feat)) if self.bias is not None: Zh = Zh + return Zh