  Community structure detection in complex networks is important since it can
help better understand the network topology and how the network works. However,
there is still not a clear and widely-accepted definition of community
structure, and in practice, different models may give very different results of
communities, making it hard to explain the results. In this paper, different
from the traditional methodologies, we design an enhanced semi-supervised
learning framework for community detection, which can effectively incorporate
the available prior information to guide the detection process and can make the
results more explainable. By logical inference, the prior information is more
fully utilized. The experiments on both the synthetic and the real-world
networks confirm the effectiveness of the framework.
