# Source code for karateclub.graph_embedding.gl2vec

```
import numpy as np
import networkx as nx
from typing import List
from karateclub.estimator import Estimator
from gensim.models.doc2vec import Doc2Vec, TaggedDocument
from karateclub.utils.treefeatures import WeisfeilerLehmanHashing
[docs]class GL2Vec(Estimator):
r"""An implementation of `"GL2Vec" <https://link.springer.com/chapter/10.1007/978-3-030-36718-3_1>`_
from the ICONIP '19 paper "GL2vec: Graph Embedding Enriched by Line Graphs with Edge Features".
First, the algorithm creates the line graph of each graph in the graph dataset.
The procedure creates Weisfeiler-Lehman tree features for nodes in graphs. Using
these features a document (graph) - feature co-occurence matrix is decomposed in order
to generate representations for the graphs.
The procedure assumes that nodes have no string feature present and the WL-hashing
defaults to the degree centrality. However, if a node feature with the key "feature"
is supported for the nodes the feature extraction happens based on the values of this key.
Args:
wl_iterations (int): Number of Weisfeiler-Lehman iterations. Default is 2.
dimensions (int): Dimensionality of embedding. Default is 128.
workers (int): Number of cores. Default is 4.
down_sampling (float): Down sampling frequency. Default is 0.0001.
epochs (int): Number of epochs. Default is 10.
learning_rate (float): HogWild! learning rate. Default is 0.025.
min_count (int): Minimal count of graph feature occurrences. Default is 5.
seed (int): Random seed for the model. Default is 42.
"""
def __init__(
self,
wl_iterations: int = 2,
dimensions: int = 128,
workers: int = 4,
down_sampling: float = 0.0001,
epochs: int = 10,
learning_rate: float = 0.025,
min_count: int = 5,
seed: int = 42,
erase_base_features: bool = False,
):
self.wl_iterations = wl_iterations
self.dimensions = dimensions
self.workers = workers
self.down_sampling = down_sampling
self.epochs = epochs
self.learning_rate = learning_rate
self.min_count = min_count
self.seed = seed
self.erase_base_features = erase_base_features
def _create_line_graph(self, graph):
r"""Getting the embedding of graphs.
Arg types:
* **graph** *(NetworkX graph)* - The graph transformed to be a line graph.
Return types:
* **line_graph** *(NetworkX graph)* - The line graph of the source graph.
"""
graph = nx.line_graph(graph)
node_mapper = {node: i for i, node in enumerate(graph.nodes())}
edges = [[node_mapper[edge[0]], node_mapper[edge[1]]] for edge in graph.edges()]
line_graph = nx.from_edgelist(edges)
return line_graph
[docs] 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()
graphs = 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,
epochs=self.epochs,
alpha=self.learning_rate,
seed=self.seed,
)
self._embedding = [model.docvecs[str(i)] for i, _ in enumerate(documents)]
[docs] def get_embedding(self) -> np.array:
r"""Getting the embedding of graphs.
Return types:
* **embedding** *(Numpy array)* - The embedding of graphs.
"""
return np.array(self._embedding)
```