  Community detection provides invaluable help for various applications, such
as marketing and product recommendation. Traditional community detection
methods designed for plain networks may not be able to detect communities with
homogeneous attributes inside on attributed networks with attribute
information. Most of recent attribute community detection methods may fail to
capture the requirements of a specific application and not be able to mine the
set of required communities for a specific application. In this paper, we aim
to detect the set of target communities in the target subspace which has some
focus attributes with large importance weights satisfying the requirements of a
specific application. In order to improve the university of the problem, we
address the problem in an extreme case where only two sample nodes in any
potential target community are provided. A Target Subspace and Communities
Mining (TSCM) method is proposed. In TSCM, a sample information extension
method is designed to extend the two sample nodes to a set of exemplar nodes
from which the target subspace is inferred. Then the set of target communities
are located and mined based on the target subspace. Experiments on synthetic
datasets demonstrate the effectiveness and efficiency of our method and
applications on real-world datasets show its application values.
