add example of deepwalk with csrgraph

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)