Abstract
Network embedding (NE) maps a network into a
low-dimensional space while preserving intrinsic
features of the network. Variational Auto-Encoder
(VAE) has been actively studied for NE. These
VAE-based methods typically utilize both network
topologies and node semantics and treat these two
types of data in the same way. However, the information of network topology and information of
node semantics are orthogonal and are often from
different sources; the former quantifies coupling relationships among nodes, whereas the latter represents node specific properties. Ignoring this difference affects NE. To address this issue, we develop
a network-specific VAE for NE, named as NetVAE.
In the encoding phase of our new approach, compression of network structures and compression of
node attributes share the same encoder in order to
perform co-training to achieve transfer learning and
information integration. In the decoding phase, a
dual decoder is introduced to reconstruct network
topologies and node attributes separately. Specifi-
cally, as a part of the dual decoder, we develop a
novel method based on a Gaussian mixture model
and the block model to reconstruct network structures. Extensive experiments on large real-world
networks demonstrate a superior performance of
the new approach over the state-of-the-art methods