SINr implementation to compute node embeddings from communities

examples/structral_node_embedding/sinr_example.py

@@ -0,0 +1,34 @@
+"""SINr illustrative example.
+Nodes in both cliques (barbell graph) will get the same embedding vectors, except for those connected to the path.
+Nodes in the path are in distinct communities with a high-enough gamma, and will thus get distinct vectors.
+"""
+
+import networkx as nx
+from karateclub.node_embedding.structural import SINr
+import matplotlib.pyplot as plt
+
+def embed_and_plot(g, gamma, ax):
+ model = SINr(gamma=gamma)
+ model.fit(g)
+ X = model.get_embedding()
+
+
+ from sklearn.decomposition import PCA
+ pca = PCA(n_components=2)
+ X_2 = pca.fit_transform(X)
+
+ ax.scatter(X_2[:,0], X_2[:,1])
+ for idx, x in enumerate(X_2):
+ ax.annotate(idx, (x[0], x[1]))
+
+
+
+g = nx.barbell_graph(4,8)
+fig, axs = plt.subplots(3)
+
+nx.draw_kamada_kawai(g, with_labels=True, ax=axs[0])
+
+embed_and_plot(g,0.5, axs[1])
+embed_and_plot(g,10, axs[2])
+
+plt.show()

karateclub/node_embedding/structural/__init__.py

@@ -1,2 +1,3 @@
 from .graphwave import GraphWave
 from .role2vec import Role2Vec
+from .sinr import SINr

karateclub/node_embedding/structural/sinr.py

@@ -0,0 +1,74 @@
+from typing import Dict, List, Set
+import networkx as nx
+from karateclub.estimator import Estimator
+from scipy.sparse import coo_matrix, csr_matrix
+from sklearn.preprocessing import normalize
+import numpy as np
+
+
+class SINr(Estimator):
+ r"""An implementation of `"SINr" <https://inria.hal.science/hal-03197434/>`_
+ from the IDA '21 best paper "SINr: Fast Computing of Sparse Interpretable Node Representations is not a Sin!".
+ The procedure performs community detection using the Louvain algorithm, and computes the distribution of edges of each node across all communities.
+ The algorithm is one of the fastest, because it mostly relies on Louvain community detection. It thus runs in quasi-linear time. Regarding space complexity, the adjacency matrix and the community membership matrix need to be stored, it is also quasi-linear.
+
+ Args:
+ gamma (int): modularity multi-resolution parameter. Default is 1. 
+ The dimension parameter does not exist for SINr, gamma should be used instead: the number of dimensions of the embedding space is based on the number of communities uncovered. The higher gamma is, the more communities are detected, the higher the number of dimensions of the latent space are uncovered. For small graphs, setting gamma to 1 is usually sufficient. For bigger graphs, it is recommended to increase gamma (5 or 10 for example). For word co-occurrence graphs, to deal with word embedding, gamma is usually set to 50 in order to get many small communities.
+ seed (int): Random seed value. Default is 42.
+ """
+
+ def __init__(
+ self,
+ gamma: int = 1,
+ seed: int = 42,
+ ):
+
+ self.gamma = gamma
+ self.seed = seed
+
+
+ def fit(self, graph: nx.classes.graph.Graph):
+ """
+ Fitting a SINr model.
+
+ Arg types:
+ * **graph** *(NetworkX graph)* - The graph to be embedded.
+ """
+ self._set_seed()
+ graph = self._check_graph(graph)
+ # Get the adjacency matrix of the graph
+ adjacency = nx.adjacency_matrix(graph)
+ norm_adjacency = normalize(adjacency, "l1") # Make rows of matrix sum at 1
+ # Detect communities use louvain algorithm with the gamma resolution parameter
+ communities = nx.community.louvain_communities(graph, resolution = self.gamma, seed = self.seed)
+ self.dimensions = len(communities)
+ # Get the community membership of the graph
+ membership_matrix = self._get_matrix_membership(communities)
+ #Computes the node-recall: for each node, the distribution of links across communities 
+ self._embedding = norm_adjacency.dot(membership_matrix)
+
+ def _get_matrix_membership(self, list_of_communities:List[Set[int]]):
+ r"""Getting the membership matrix describing for each node (rows), in which community (column) it belongs.
+
+ Return types:
+ * **Membership matrix** *(scipy sparse matrix csr)* - Size nodes, communities
+ """
+ row = list()
+ col = list()
+ data = list()
+ for idx_c, community in enumerate(list_of_communities):
+ for node in community:
+ row.append(node)
+ col.append(idx_c)
+ data.append(1)
+ return coo_matrix((data, (row, col)), shape=(len(row), len(list_of_communities))).tocsr()
+
+
+ def get_embedding(self) -> np.array:
+ r"""Getting the node embedding.
+
+ Return types:
+ * **embedding** *(Numpy array)* - The embedding of nodes.
+ """
+ return self._embedding.toarray()

test/structral_node_embedding_test.py

@@ -1,6 +1,6 @@
 import numpy as np
 import networkx as nx
-from karateclub import Role2Vec, GraphWave
+from karateclub import Role2Vec, GraphWave, SINr
 
 
 def test_role2vec():
@@ -73,3 +73,36 @@ def test_graphwave():
  assert embedding.shape[0] == graph.number_of_nodes()
  assert embedding.shape[1] == 2 * model.sample_number
  assert type(embedding) == np.ndarray
+
+
+
+def test_sinr():
+ """
+ Testing the SINr class.
+ """
+ model = SINr()
+
+ graph = nx.watts_strogatz_graph(100, 10, 0.5)
+
+ model.fit(graph)
+
+ embedding = model.get_embedding()
+
+ assert embedding.shape[0] == graph.number_of_nodes()
+ assert embedding.shape[1] == model.dimensions
+ assert type(embedding) == np.ndarray
+
+ model = SINr(gamma=5)
+
+ graph = nx.watts_strogatz_graph(200, 10, 0.5)
+
+ model.fit(graph)
+
+ embedding = model.get_embedding()
+
+ assert embedding.shape[0] == graph.number_of_nodes()
+ assert embedding.shape[1] == model.dimensions
+ model2 = SINr(gamma=10)
+ model2.fit(graph)
+ assert model2.dimensions > model.dimensions
+ assert type(embedding) == np.ndarray