def convert_ensmallen_graph_to_networkx_graph(graph: Graph) -> nx.Graph:
"""Return networkX graph derived from the provided Ensmallen Graph.
Parameters
-----------
graph: Graph
The graph to be converted.
"""
if graph.is_directed():
result_graph = nx.DiGraph()
else:
result_graph = nx.Graph()
result_graph.add_nodes_from(graph.get_node_ids())
if graph.has_edge_weights():
result_graph.add_weighted_edges_from([
(src_name, dst_name, edge_weight)
for (src_name, dst_name), edge_weight in zip(
graph.get_directed_edge_node_ids(), graph.get_edge_weights())
])
else:
result_graph.add_edges_from(
graph.get_edge_node_ids(directed=graph.is_directed()))
return result_graph
def graph_to_sparse_tensor(
graph: Graph,
use_weights: bool,
use_simmetric_normalized_laplacian: bool,
handling_multi_graph: str = "warn",
) -> tf.SparseTensor:
"""Returns provided graph as sparse Tensor.
Parameters
-------------------
graph: Graph,
The graph to convert.
use_weights: bool,
Whether to load the graph weights.
use_simmetric_normalized_laplacian: bool
Whether to use the symmetrically normalized laplacian
handling_multi_graph: str = "warn"
How to behave when dealing with multigraphs.
Possible behaviours are:
- "warn", which warns the user and drops the multi-edges.
- "raise"
- "drop"
Raises
-------------------
ValueError,
If the weights are requested but the graph does not contain any.
ValueError,
If the graph contains singletons.
ValueError,
If the graph is a multigraph.
Returns
-------------------
SparseTensor with (weighted) adjacency matrix.
"""
if use_weights and not graph.has_edge_weights():
raise ValueError("Edge weights were requested but the provided graph "
"does not contain any edge weight.")
if graph.has_singleton_nodes():
raise ValueError(
f"In the provided {graph.get_name()} graph there are "
f"{graph.get_number_of_singleton_nodes()} singleton nodes."
"The GCN model does not support operations on graph containing "
"singletons. You can either choose to drop singletons from "
"the graph by using the `graph.remove_singleton_nodes()` "
"method or alternatively you can add selfloops to them by "
"using the `graph.add_selfloops()` method.")
if graph.is_multigraph():
message = (
"The GCN model does not currently support convolutions on a multigraph. "
"We are dropping the parallel edges before computing the adjacency matrix."
)
if handling_multi_graph == "warn":
warnings.warn(message)
elif handling_multi_graph == "raise":
raise ValueError(message)
graph = graph.remove_parallel_edges()
if use_simmetric_normalized_laplacian:
edge_node_ids, weights = graph.get_symmetric_normalized_laplacian_coo_matrix(
)
return tf.sparse.reorder(
tf.SparseTensor(
edge_node_ids, np.abs(weights),
(graph.get_number_of_nodes(), graph.get_number_of_nodes())))
return tf.SparseTensor(
graph.get_directed_edge_node_ids(),
(graph.get_edge_weights()
if use_weights else tf.ones(graph.get_number_of_directed_edges())),
(graph.get_number_of_nodes(), graph.get_number_of_nodes()))
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])
