I got this issue using struc2vec and node2vec methods
`---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
in
1 model_struc2vec = ge.Struc2Vec(G, 10, 80, workers=4, verbose=40, ) #init model
----> 2 model_struc2vec.train(window_size = 5, iter = 3)# train model
3 embeddings_struc2vec = model_struc3vec.get_embeddings()# get embedding vectors

/anaconda3/envs/python36/lib/python3.6/site-packages/ge-0.0.0-py3.6.egg/ge/models/struc2vec.py in train(self, embed_size, window_size, workers, iter)
114 print("Learning representation...")
115 model = Word2Vec(sentences, size=embed_size, window=window_size, min_count=0, hs=1, sg=1, workers=workers,
--> 116 iter=iter)
117 print("Learning representation done!")
118 self.w2v_model = model

/anaconda3/envs/python36/lib/python3.6/site-packages/gensim-3.6.0-py3.6-macosx-10.7-x86_64.egg/gensim/models/word2vec.py in init(self, sentences, corpus_file, size, alpha, window, min_count, max_vocab_size, sample, seed, workers, min_alpha, sg, hs, negative, ns_exponent, cbow_mean, hashfxn, iter, null_word, trim_rule, sorted_vocab, batch_words, compute_loss, callbacks, max_final_vocab)
765 callbacks=callbacks, batch_words=batch_words, trim_rule=trim_rule, sg=sg, alpha=alpha, window=window,
766 seed=seed, hs=hs, negative=negative, cbow_mean=cbow_mean, min_alpha=min_alpha, compute_loss=compute_loss,
--> 767 fast_version=FAST_VERSION)
768
769 def _do_train_epoch(self, corpus_file, thread_id, offset, cython_vocab, thread_private_mem, cur_epoch,

/anaconda3/envs/python36/lib/python3.6/site-packages/gensim-3.6.0-py3.6-macosx-10.7-x86_64.egg/gensim/models/base_any2vec.py in init(self, sentences, corpus_file, workers, vector_size, epochs, callbacks, batch_words, trim_rule, sg, alpha, window, seed, hs, negative, ns_exponent, cbow_mean, min_alpha, compute_loss, fast_version, **kwargs)
757 raise TypeError("You can't pass a generator as the sentences argument. Try an iterator.")
758
--> 759 self.build_vocab(sentences=sentences, corpus_file=corpus_file, trim_rule=trim_rule)
760 self.train(
761 sentences=sentences, corpus_file=corpus_file, total_examples=self.corpus_count,

/anaconda3/envs/python36/lib/python3.6/site-packages/gensim-3.6.0-py3.6-macosx-10.7-x86_64.egg/gensim/models/base_any2vec.py in build_vocab(self, sentences, corpus_file, update, progress_per, keep_raw_vocab, trim_rule, **kwargs)
941 trim_rule=trim_rule, **kwargs)
942 report_values['memory'] = self.estimate_memory(vocab_size=report_values['num_retained_words'])
--> 943 self.trainables.prepare_weights(self.hs, self.negative, self.wv, update=update, vocabulary=self.vocabulary)
944
945 def build_vocab_from_freq(self, word_freq, keep_raw_vocab=False, corpus_count=None, trim_rule=None, update=False):

/anaconda3/envs/python36/lib/python3.6/site-packages/gensim-3.6.0-py3.6-macosx-10.7-x86_64.egg/gensim/models/word2vec.py in prepare_weights(self, hs, negative, wv, update, vocabulary)
1820 # set initial input/projection and hidden weights
1821 if not update:
-> 1822 self.reset_weights(hs, negative, wv)
1823 else:
1824 self.update_weights(hs, negative, wv)

/anaconda3/envs/python36/lib/python3.6/site-packages/gensim-3.6.0-py3.6-macosx-10.7-x86_64.egg/gensim/models/word2vec.py in reset_weights(self, hs, negative, wv)
1837 for i in xrange(len(wv.vocab)):
1838 # construct deterministic seed from word AND seed argument
-> 1839 wv.vectors[i] = self.seeded_vector(wv.index2word[i] + str(self.seed), wv.vector_size)
1840 if hs:
1841 self.syn1 = zeros((len(wv.vocab), self.layer1_size), dtype=REAL)

TypeError: unsupported operand type(s) for +: 'int' and 'str'
`