def _fit_transform(self,
graph: Graph,
return_dataframe: bool = True,
verbose: bool = True) -> EmbeddingResult:
"""Return node embedding."""
number_of_nodes = graph.get_number_of_nodes()
embedding = eigh(graph.get_dense_modularity_matrix(),
eigvals=(number_of_nodes - self._embedding_size,
number_of_nodes - 1))[1]
if return_dataframe:
node_names = graph.get_node_names()
embedding = pd.DataFrame(embedding, index=node_names)
return EmbeddingResult(embedding_method_name=self.model_name(),
node_embeddings=embedding)
def _fit_transform(self,
graph: Graph,
return_dataframe: bool = True,
verbose: bool = True) -> EmbeddingResult:
"""Return node embedding."""
matrix = None
if self._metric == "Jaccard":
edges, weights = graph.get_jaccard_coo_matrix()
elif self._metric == "Laplacian":
edges, weights = graph.get_laplacian_coo_matrix()
elif self._metric == "Modularity":
matrix = graph.get_dense_modularity_matrix()
elif self._metric == "Left Normalized Laplacian":
edges, weights = graph.get_left_normalized_laplacian_coo_matrix()
elif self._metric == "Right Normalized Laplacian":
edges, weights = graph.get_right_normalized_laplacian_coo_matrix()
elif self._metric == "Symmetric Normalized Laplacian":
edges, weights = graph.get_symmetric_normalized_laplacian_coo_matrix(
)
elif self._metric == "Neighbours Intersection size":
edges, weights = graph.get_neighbours_intersection_size_coo_matrix(
)
elif self._metric == "Ancestors Jaccard":
matrix = graph.get_shared_ancestors_jaccard_adjacency_matrix(
graph.get_breadth_first_search_from_node_names(
src_node_name=self._root_node_name,
compute_predecessors=True),
verbose=verbose)
elif self._metric == "Ancestors size":
matrix = graph.get_shared_ancestors_size_adjacency_matrix(
graph.get_breadth_first_search_from_node_names(
src_node_name=self._root_node_name,
compute_predecessors=True),
verbose=verbose)
elif self._metric == "Adamic-Adar":
edges, weights = graph.get_adamic_adar_coo_matrix()
elif self._metric == "Adjacency":
edges, weights = graph.get_directed_edge_node_ids(), np.ones(
graph.get_number_of_directed_edges())
else:
raise NotImplementedError(f"The provided metric {self._metric} "
"is not currently supported.")
if matrix is None:
matrix = coo_matrix((weights, (edges[:, 0], edges[:, 1])),
shape=(graph.get_number_of_nodes(),
graph.get_number_of_nodes()),
dtype=np.float32)
U, sigmas, Vt = sparse_svds(matrix,
k=int(self._embedding_size / 2))
else:
U, sigmas, Vt = randomized_svd(matrix,
n_components=int(
self._embedding_size / 2))
sigmas = np.diagflat(np.sqrt(sigmas))
left_embedding = np.dot(U, sigmas)
right_embedding = np.dot(Vt.T, sigmas)
if return_dataframe:
node_names = graph.get_node_names()
left_embedding = pd.DataFrame(left_embedding, index=node_names)
right_embedding = pd.DataFrame(right_embedding, index=node_names)
return EmbeddingResult(
embedding_method_name=self.model_name(),
node_embeddings=[left_embedding, right_embedding])