Source code for cogdl.models.emb.spectral

import numpy as np
import networkx as nx
import scipy.sparse as sp
from sklearn import preprocessing
from .. import BaseModel

[docs]class Spectral(BaseModel): r"""The Spectral clustering model from the `"Leveraging social media networks for classification" <>`_ paper Args: hidden_size (int) : The dimension of node representation. """
[docs] @staticmethod def add_args(parser): """Add model-specific arguments to the parser.""" # fmt: off parser.add_argument("--hidden-size", type=int, default=128)
# fmt: on
[docs] @classmethod def build_model_from_args(cls, args): return cls(args.hidden_size)
def __init__(self, hidden_size): super(Spectral, self).__init__() self.dimension = hidden_size
[docs] def forward(self, graph, return_dict=False): nx_g = graph.to_networkx() matrix = nx.normalized_laplacian_matrix(nx_g).todense() matrix = np.eye(matrix.shape[0]) - np.asarray(matrix) ut, s, _ = sp.linalg.svds(matrix, self.dimension) emb_matrix = ut * np.sqrt(s) embeddings = preprocessing.normalize(emb_matrix, "l2") if return_dict: features_matrix = dict() for vid, node in enumerate(nx_g.nodes()): features_matrix[node] = embeddings[vid] else: features_matrix = np.zeros((graph.num_nodes, embeddings.shape[1])) nx_nodes = nx_g.nodes() features_matrix[nx_nodes] = embeddings[np.arange(graph.num_nodes)] return features_matrix