Source code for karateclub.graph_embedding.fgsd

import numpy as np
import networkx as nx
from typing import List
from karateclub.estimator import Estimator

[docs]class FGSD(Estimator): r"""An implementation of `"FGSD" <https://papers.nips.cc/paper/6614-hunt-for-the-unique-stable-sparse-and-fast-feature-learning-on-graphs>`_ from the NeurIPS '17 paper "Hunt For The Unique, Stable, Sparse And Fast Feature Learning On Graphs". The procedure calculates the Moore-Penrose spectrum of the normalized Laplacian. Using this spectrum the histogram of the spectral features is used as a whole graph representation. Args: hist_bins (int): Number of histogram bins. Default is 200. hist_range (int): Histogram range considered. Default is 20. seed (int): Random seed value. Default is 42. """ def __init__(self, hist_bins: int = 200, hist_range: int = 20, seed: int = 42): self.hist_bins = hist_bins self.hist_range = (0, hist_range) self.seed = seed def _calculate_fgsd(self, graph): """ Calculating the features of a graph. Arg types: * **graph** *(NetworkX graph)* - A graph to be embedded. Return types: * **hist** *(Numpy array)* - The embedding of a single graph. """ L = nx.normalized_laplacian_matrix(graph).todense() fL = np.linalg.pinv(L) ones = np.ones(L.shape) S = np.outer(np.diag(fL), ones) + np.outer(ones, np.diag(fL)) - 2 * fL hist, bin_edges = np.histogram( S.flatten(), bins=self.hist_bins, range=self.hist_range ) return hist
[docs] def fit(self, graphs: List[nx.classes.graph.Graph]): """ Fitting a FGSD model. Arg types: * **graphs** *(List of NetworkX graphs)* - The graphs to be embedded. """ self._set_seed() graphs = self._check_graphs(graphs) self._embedding = [self._calculate_fgsd(graph) for graph in graphs]
[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)