Model¶
In this section, we will create a spectral clustering model, which is a very simple graph embedding algorithm. We name it spectral.py and put it in cogdl/models/emb directory.
First we import necessary library like numpy, scipy, networkx, sklearn, we also import API like ‘BaseModel’ and ‘register_model’ from cogl/models/ to build our new model:
import numpy as np
import networkx as nx
import scipy.sparse as sp
from sklearn import preprocessing
from .. import BaseModel, register_model
Then we use function decorator to declare new model for CogDL
@register_model('spectral')
class Spectral(BaseModel):
(...)
We have to implement method ‘build_model_from_args’ in spectral.py. If it need more parameters to train, we can use ‘add_args’ to add model-specific arguments.
@staticmethod
def add_args(parser):
"""Add model-specific arguments to the parser."""
pass
@classmethod
def build_model_from_args(cls, args):
return cls(args.hidden_size)
def __init__(self, dimension):
super(Spectral, self).__init__()
self.dimension = dimension
Each new model should provide a ‘train’ method to obtain representation.
def train(self, G):
matrix = nx.normalized_laplacian_matrix(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)
emb_matrix = preprocessing.normalize(emb_matrix, "l2")
return emb_matrix
All implemented models are at https://github.com/THUDM/cogdl/tree/master/cogdl/models.