Detecting Overlapping Communities of Nodes with Multiple Attributes from Heterogeneous Networks

Many methods have been proposed for detecting communities from heterogeneous information networks with general topologies. However, most of these methods can detect communities with homogeneous structures containing nodes with only a single attribute. Investigating methods for detecting communities containing nodes with multiple attributes from heterogeneous information networks with general topologies has been understudied. Such communities are realistic in real-world social structures and exhibits many interesting properties. Towards this, we propose a system called DOMAIN that can detect overlapping communities of nodes with multiple attributes from heterogeneous information networks with general topologies. The framework of DOMAIN focuses on domains (i.e., attributes) that describe human characteristics such as ethnicity, culture, religion, demographic, age, or the like. The ultimate objective of the framework is to detect the smallest sub-communities with the largest possible number of domains, to which an active user belongs. The smaller a sub-community is, the more specific and granular its interests are. The interests of such a sub-community is the union of the interests and characteristics of the single domain communities, from which it is constructed. We evaluated DOMAIN by comparing it experimentally with three methods. Results revealed marked improvement.