Inproceedings,
Co-embedding Attributed Networks.pdf
Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining - WSDM '19, Melbourne VIC, Australia, ACM Press, (2019)
DOI: 10.1145/3289600.3291015
Abstract
Existing embedding methods for attributed networks aim at learning
low-dimensional vector representations for nodes only but not
for both nodes and attributes, resulting in the fact that they cannot
capture the affinities between nodes and attributes. However, capturing
such affinities is of great importance to the success of many
real-world attributed network applications, such as attribute inference
and user profiling. Accordingly, in this paper, we introduce a
Co-embedding model for Attributed Networks (CAN), which learns
low-dimensional representations of both attributes and nodes in
the same semantic space such that the affinities between them
can be effectively captured and measured. To obtain high-quality
embeddings, we propose a variational auto-encoder that embeds
each node and attribute with means and variances of Gaussian
distributions. Experimental results on real-world networks demonstrate
that our model yields excellent performance in a number of
applications compared with state-of-the-art techniques.
