  Network embedding is a method to learn low-dimensional representation vectors
for nodes in complex networks. In real networks, nodes may have multiple tags
but existing methods ignore the abundant semantic and hierarchical information
of tags. This information is useful to many network applications and usually
very stable. In this paper, we propose a tag representation learning model,
Tag2Vec, which mixes nodes and tags into a hybrid network. Firstly, for tag
networks, we define semantic distance as the proximity between tags and design
a novel strategy, parameterized random walk, to generate context with semantic
and hierarchical information of tags adaptively. Then, we propose hyperbolic
Skip-gram model to express the complex hierarchical structure better with lower
output dimensions. We evaluate our model on the NBER U.S. patent dataset and
WordNet dataset. The results show that our model can learn tag representations
with rich semantic information and it outperforms other baselines.
