Title: Mining Application-aware Community Organization with Expanded Feature Subspaces from Concerned Attributes in Social Networks,
Abstract: Social networks are typical attributed networks with node attributes.
Different from traditional attribute community detection problem aiming at
obtaining the whole set of communities in the network, we study an
application-oriented problem of mining an application-aware community
organization with respect to specific concerned attributes. The concerned
attributes are designated based on the requirements of any application by a
user in advance. The application-aware community organization w.r.t. concerned
attributes consists of the communities with feature subspaces containing these
concerned attributes. Besides concerned attributes, feature subspace of each
required community may contain some other relevant attributes. All relevant
attributes of a feature subspace jointly describe and determine the community
embedded in such subspace. Thus the problem includes two subproblems, i.e., how
to expand the set of concerned attributes to complete feature subspaces and how
to mine the communities embedded in the expanded subspaces. Two subproblems are
jointly solved by optimizing a quality function called subspace fitness. An
algorithm called ACM is proposed. In order to locate the communities
potentially belonging to the application-aware community organization, cohesive
parts of a network backbone composed of nodes with similar concerned attributes
are detected and set as the community seeds. The set of concerned attributes is
set as the initial subspace for all community seeds. Then each community seed
and its attribute subspace are adjusted iteratively to optimize the subspace
fitness. Extensive experiments on synthetic datasets demonstrate the
effectiveness and efficiency of our method and applications on real-world
networks show its application values.