Abstract
Network embedding is an effective approach to
learn the low-dimensional representations of vertices in networks, aiming to capture and preserve
the structure and inherent properties of networks.
The vast majority of existing network embedding
methods exclusively focus on vertex proximity of
networks, while ignoring the network internal community structure. However, the homophily principle indicates that vertices within the same community are more similar to each other than those
from different communities, thus vertices within
the same community should have similar vertex
representations. Motivated by this, we propose
a novel network embedding framework NECS to
learn the Network Embedding with Community
Structural information, which preserves the highorder proximity and incorporates the community
structure in vertex representation learning. We formulate the problem into a principled optimization
framework and provide an effective alternating algorithm to solve it. Extensive experimental results
on several benchmark network datasets demonstrate the effectiveness of the proposed framework
in various network analysis tasks including network
reconstruction, link prediction and vertex classifi-
cation