  Network representation learning (NRL) aims to learn low-dimensional vectors
for vertices in a network. Most existing NRL methods focus on learning
representations from local context of vertices (such as their neighbors).
Nevertheless, vertices in many complex networks also exhibit significant global
patterns widely known as communities. It's intuitive that vertices in the same
community tend to connect densely and share common attributes. These patterns
are expected to improve NRL and benefit relevant evaluation tasks, such as link
prediction and vertex classification. Inspired by the analogy between network
representation learning and text modeling, we propose a unified NRL framework
by introducing community information of vertices, named as Community-enhanced
Network Representation Learning (CNRL). CNRL simultaneously detects community
distribution of each vertex and learns embeddings of both vertices and
communities. Moreover, the proposed community enhancement mechanism can be
applied to various existing NRL models. In experiments, we evaluate our model
on vertex classification, link prediction, and community detection using
several real-world datasets. The results demonstrate that CNRL significantly
and consistently outperforms other state-of-the-art methods while verifying our
assumptions on the correlations between vertices and communities.
