Fig. 4
From: Learning compact graph representations via an encoder-decoder network
A random walk over a graph can be split into three sub-sequences (s1,s2,s3). The middle sequence is fed as input into the encoder and the decoders attempt to reconstruct the other sub-sequences. Note that the unattached arrows are connected to the encoder output to condition the decoder prediction at each step. Since the model processes entire sequences, it can distinguish the structural difference between the two neighboring sub-structures, even though they share the same types of nodes