.

examples/deepwalk_wiki_csrgraph.py

@@ -0,0 +1,53 @@
+
+import numpy as np
+
+from ge.classify import read_node_label, Classifier
+from ge import DeepWalk
+from sklearn.linear_model import LogisticRegression
+
+import matplotlib.pyplot as plt
+import networkx as nx
+from sklearn.manifold import TSNE
+
+
+def evaluate_embeddings(embeddings):
+    X, Y = read_node_label('../data/wiki/wiki_labels.txt')
+    tr_frac = 0.8
+    print("Training classifier using {:.2f}% nodes...".format(
+        tr_frac * 100))
+    clf = Classifier(embeddings=embeddings, clf=LogisticRegression())
+    clf.split_train_evaluate(X, Y, tr_frac)
+
+
+def plot_embeddings(embeddings,):
+    X, Y = read_node_label('../data/wiki/wiki_labels.txt')
+
+    emb_list = []
+    for k in X:
+        emb_list.append(embeddings[k])
+    emb_list = np.array(emb_list)
+
+    model = TSNE(n_components=2)
+    node_pos = model.fit_transform(emb_list)
+
+    color_idx = {}
+    for i in range(len(X)):
+        color_idx.setdefault(Y[i][0], [])
+        color_idx[Y[i][0]].append(i)
+
+    for c, idx in color_idx.items():
+        plt.scatter(node_pos[idx, 0], node_pos[idx, 1], label=c)
+    plt.legend()
+    plt.show()
+
+
+if __name__ == "__main__":
+    G = nx.read_edgelist('../data/wiki/Wiki_edgelist.txt',
+                         create_using=nx.DiGraph(), nodetype=None, data=[('weight', int)])
+
+    model = DeepWalk(G, walk_length=10, num_walks=80, workers=1, use_csrgraph=True)
+    model.train(window_size=5, iter=3)
+    embeddings = model.get_embeddings()
+
+    evaluate_embeddings(embeddings)
+    #plot_embeddings(embeddings)

ge/models/deepwalk.py

@@ -20,36 +20,70 @@
 from ..walker import RandomWalker
 from gensim.models import Word2Vec
 import pandas as pd
-
-
+import numpy as np
+from csrgraph import csrgraph
 class DeepWalk:
-    def __init__(self, graph, walk_length, num_walks, workers=1):
-
-        self.graph = graph
+    def __init__(self, graph, walk_length, num_walks, workers=1,use_csrgraph=False):
+        self.use_csrgraph=use_csrgraph
         self.w2v_model = None
         self._embeddings = {}
-
-        self.walker = RandomWalker(
-            graph, p=1, q=1, )
-        self.sentences = self.walker.simulate_walks(
-            num_walks=num_walks, walk_length=walk_length, workers=workers, verbose=1)
+
+        if self.use_csrgraph:
+            node_names=list(graph.nodes())
+            self.graph=csrgraph(graph,nodenames=node_names,threads=workers)
+
+            self.sentences = pd.DataFrame(self.graph.random_walks(
+                epochs=num_walks, walklen=walk_length,  return_weight=1.,neighbor_weight=1.))
+            # Map nodeId -> node name
+            node_dict = dict(zip(np.arange(len(node_names)), node_names))
+
+            for col in self.sentences.columns:
+                self.sentences[col] = self.sentences[col].map(node_dict).astype(str)
+            # Somehow gensim only trains on this list iterator
+            # it silently mistrains on array input
+            self.sentences = [list(x) for x in self.sentences.itertuples(False, None)]
+
+        else:
+            self.graph = graph
+            self.walker = RandomWalker(
+                graph, p=1, q=1, )
+            self.sentences = self.walker.simulate_walks(
+                num_walks=num_walks, walk_length=walk_length, workers=workers, verbose=1)
 
     def train(self, embed_size=128, window_size=5, workers=3, iter=5, **kwargs):
-
-        kwargs["sentences"] = self.sentences
-        kwargs["min_count"] = kwargs.get("min_count", 0)
-        kwargs["size"] = embed_size
-        kwargs["sg"] = 1  # skip gram
-        kwargs["hs"] = 1  # deepwalk use Hierarchical Softmax
-        kwargs["workers"] = workers
-        kwargs["window"] = window_size
-        kwargs["iter"] = iter
-
-        print("Learning embedding vectors...")
-        model = Word2Vec(**kwargs)
-        print("Learning embedding vectors done!")
-
-        self.w2v_model = model
+
+
+        if self.use_csrgraph:
+            kwargs["sentences"] = self.sentences
+            kwargs["min_count"] = kwargs.get("min_count", 0)
+            kwargs["vector_size"] = embed_size
+            kwargs["sg"] = 1  # skip gram
+            kwargs["hs"] = 1  # deepwalk use Hierarchical Softmax
+            kwargs["workers"] = workers
+            kwargs["window"] = window_size
+            kwargs["epochs"] = iter
+
+            print("Learning embedding vectors...")
+            model = Word2Vec(**kwargs)
+            print("Learning embedding vectors done!")
+            self.w2v_model = model
+
+        else:
+            kwargs["sentences"] = self.sentences
+            kwargs["min_count"] = kwargs.get("min_count", 0)
+            kwargs["vector_size"] = embed_size
+            kwargs["sg"] = 1  # skip gram
+            kwargs["hs"] = 1  # deepwalk use Hierarchical Softmax
+            kwargs["workers"] = workers
+            kwargs["window"] = window_size
+            kwargs["epochs"] = iter
+
+            print("Learning embedding vectors...")
+            model = Word2Vec(**kwargs)
+            print("Learning embedding vectors done!")
+
+            self.w2v_model = model
+
         return model
 
     def get_embeddings(self,):