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" <>`_ 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)