Generation of new graph from trained GVAE about gae HOT 3 OPEN

You can sample from a standard normal distribution directly after training to generate new graphs

from gae.

Dear Haorannlp,
as far as i understood,the way in which I can use this model is the following:

1. emb = sess.run(model.z_mean, feed_dict=feed_dict)
2. adj_rec = np.dot(emb, emb.T)

Now, the problem is that after training the model, I would like to generate new instances without using any graph as input of the model. In order to do this I need to produce the "emb" and perform the dot product. Now, I cannot figure out how can I obtain this "emb" just by sampling from a strandard Normal distribution. In fact, from the code I see that:

self.z = self.z_mean + tf.random_normal([self.n_samples, FLAGS.hidden2]) * tf.exp(self.z_log_std)

is the part that should be distributed as normal after training. Am I understood correctly?
If the answer is yes and I sample from a normal distribution, I will obtain "z" and not "z_mean". Is that right?

from gae.

Sampling from a standard normal distribution : self.z = tf.random_normal([1, FLAGS.hidden2]). You don't need encoder to generate new graphs, only the decoder is enough.

from gae.