# Source code for karateclub.node_embedding.neighbourhood.walklets

```
import numpy as np
import networkx as nx
from gensim.models.word2vec import Word2Vec
from karateclub.utils.walker import RandomWalker
from karateclub.estimator import Estimator
[docs]class Walklets(Estimator):
r"""An implementation of `"Walklets" <https://arxiv.org/abs/1605.02115>`_
from the ASONAM '17 paper "Don't Walk, Skip! Online Learning of Multi-scale
Network Embeddings". The procedure uses random walks to approximate the
pointwise mutual information matrix obtained by individual normalized
adjacency matrix powers. These are all decomposed by an approximate
factorization technique and the embeddings are concatenated together.
Args:
walk_number (int): Number of random walks. Default is 10.
walk_length (int): Length of random walks. Default is 80.
dimensions (int): Dimensionality of embedding. Default is 32.
workers (int): Number of cores. Default is 4.
window_size (int): Matrix power order. Default is 4.
epochs (int): Number of epochs. Default is 1.
learning_rate (float): HogWild! learning rate. Default is 0.05.
min_count (int): Minimal count of node occurrences. Default is 1.
seed (int): Random seed value. Default is 42.
"""
def __init__(
self,
walk_number: int = 10,
walk_length: int = 80,
dimensions: int = 32,
workers: int = 4,
window_size: int = 4,
epochs: int = 1,
learning_rate: float = 0.05,
min_count: int = 1,
seed: int = 42,
):
self.walk_number = walk_number
self.walk_length = walk_length
self.dimensions = dimensions
self.workers = workers
self.window_size = window_size
self.epochs = epochs
self.learning_rate = learning_rate
self.min_count = min_count
self.seed = seed
def _select_walklets(self, walks, power):
walklets = []
for walk in walks:
for step in range(power + 1):
neighbors = [n for i, n in enumerate(walk[step:]) if i % power == 0]
walklets.append(neighbors)
return walklets
[docs] def fit(self, graph: nx.classes.graph.Graph):
"""
Fitting a Walklets 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)
num_of_nodes = graph.number_of_nodes()
self._embedding = []
for power in range(1, self.window_size + 1):
walklets = self._select_walklets(walker.walks, power)
model = Word2Vec(
walklets,
hs=0,
alpha=self.learning_rate,
epochs=self.epochs,
vector_size=self.dimensions,
window=1,
min_count=self.min_count,
workers=self.workers,
seed=self.seed,
)
embedding = np.array([model.wv[str(n)] for n in range(num_of_nodes)])
self._embedding.append(embedding)
[docs] def get_embedding(self) -> np.array:
r"""Getting the node embedding.
Return types:
* **embedding** *(Numpy array)* - The embedding of nodes.
"""
return np.concatenate(self._embedding, axis=1)
```