Advancing community detection using Keyword Attribute Search

Abstract

As social network structures evolve constantly, it is necessary to design an efficient mechanism to track the influential nodes and accurate communities in the networks. The attributed graph represents the information about properties of the nodes and relationships between different nodes, hence, this attribute information can be used for more accurate community detection. Current techniques of community detection do not consider the attribute or keyword information associated with the nodes in a graph. In this paper, we propose a novel algorithm of online community detection using a technique of keyword search over the attributed graph. First, the influential attributes are derived based on the probability of occurrence of each attribute type-value pair on all nodes and edges, respectively. Then, a compact Keyword Attribute Signature is created for each node based on the unique id of each influential attribute. The attributes on each node are classified into different classes, and this class information is assigned on each node to derive the strongest association among different nodes. Once the class information is assigned to all the nodes, we use a keyword search technique to derive a community of nodes belonging to the same class. The keyword search technique makes it possible to search community of nodes in an online and computationally efficient manner compared to the existing techniques. The experimental analysis shows that the proposed method derive the community of nodes in an online manner. The nodes in a community are strongly connected to each other and share common attributes. Thus, the community detection can be advanced by using keyword search method, which allows personalized and generalized communities to be retrieved in an online manner.