def fit(self, graph: nx.classes.graph.Graph):
"""
Fitting a Role2vec model.
Arg types:
* **graph** *(NetworkX graph)* - The graph to be embedded.
"""
self._set_seed()
self._check_graph(graph)
walker = RandomWalker(self.walk_length, self.walk_number)
walker.do_walks(graph)
hasher = WeisfeilerLehmanHashing(
graph=graph,
wl_iterations=self.wl_iterations,
attributed=False,
erase_base_features=self.erase_base_features)
node_features = hasher.get_node_features()
documents = self._create_documents(walker.walks, node_features)
model = Doc2Vec(documents,
vector_size=self.dimensions,
window=0,
min_count=self.min_count,
dm=0,
workers=self.workers,
sample=self.down_sampling,
iter=self.epochs,
alpha=self.learning_rate,
seed=self.seed)
self._embedding = [
model.docvecs[str(i)] for i, _ in enumerate(documents)
]
def fit(self, graphs: List[nx.classes.graph.Graph]):
"""
Fitting a GL2Vec model.
Arg types:
* **graphs** *(List of NetworkX graphs)* - The graphs to be embedded.
"""
self._set_seed()
self._check_graphs(graphs)
graphs = [self._create_line_graph(graph) for graph in graphs]
documents = [
WeisfeilerLehmanHashing(graph, self.wl_iterations, False,
self.erase_base_features)
for graph in graphs
]
documents = [
TaggedDocument(words=doc.get_graph_features(), tags=[str(i)])
for i, doc in enumerate(documents)
]
model = Doc2Vec(documents,
vector_size=self.dimensions,
window=0,
min_count=self.min_count,
dm=0,
sample=self.down_sampling,
workers=self.workers,
iter=self.epochs,
alpha=self.learning_rate,
seed=self.seed)
self._embedding = [
model.docvecs[str(i)] for i, _ in enumerate(documents)
]
def fit(self, graphs):
"""
Fitting a Graph2Vec model.
Arg types:
* **graphs** *(List of NetworkX graphs)* - The graphs to be embedded.
"""
documents = [
WeisfeilerLehmanHashing(graph, self.wl_iterations, self.attributed)
for graph in graphs
]
documents = [
TaggedDocument(words=doc.extracted_features, tags=[str(i)])
for i, doc in enumerate(documents)
]
model = Doc2Vec(documents,
vector_size=self.dimensions,
window=0,
min_count=self.min_count,
dm=0,
sample=self.down_sampling,
workers=self.workers,
epochs=self.epochs,
alpha=self.learning_rate)
self._embedding = [
model.docvecs[str(i)] for i, _ in enumerate(documents)
]
def fit(self, graphs: List[nx.classes.graph.Graph]):
self._set_seed()
graphs = self._check_graphs(graphs)
documents = [WeisfeilerLehmanHashing(graph, self.wl_iterations, self.attributed, self.erase_base_features) for
graph in graphs]
documents = [TaggedDocument(words=doc.get_graph_features(), tags=[str(i)]) for i, doc in enumerate(documents)]
model = Doc2Vec(documents,
vector_size=self.dimensions,
window=0,
min_count=self.min_count,
dm=0,
sample=self.down_sampling,
workers=self.workers,
epochs=self.epochs,
alpha=self.learning_rate,
seed=self.seed)
self._embedding = model