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"
<http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.481.5392&rep=rep1&type=pdf>`_ 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