Abstract
Community detection in social networks is becoming one of the key tasks in social network analysis, since it helps with analyzing groups of users with similar interests. As a consequence, it is possible to detect radicalism or even reduce the size of the data to be analyzed, among other applications. This paper presents a metaheuristic approach based on Greedy Randomized Adaptive Search Procedure (GRASP) methodology for detecting communities in social networks. The community detection problem is modeled as an optimization problem, where the objective function to be optimized is the modularity of the network, a well-known metric in this scientific field. The results obtained outperform classical methods of community detection over a set of real-life instances with respect to the quality of the communities detected.
Highlights
The evolution of social networks in the last few decades has aroused the interest of scientists from different and diverse areas, from psychology to computer sciences
The constructive procedure designed for the community detection problem, named GRASPAGG
We consider the conductance with the aim of testing the robustness of the methods when including one additional metric
Summary
The evolution of social networks in the last few decades has aroused the interest of scientists from different and diverse areas, from psychology to computer sciences. Millions of people constantly share all their personal and professional information in several social networks [1]. Social networks have become one of the most used information sources, mainly due to their ability to provide the user with real-time content. Social networks are a new way of communication, and a powerful tool that can be used to gather information related to relevant questions. Extracting relevant information from social networks is a matter of interest mainly due to the huge amount of potential data available. Traditional network analysis techniques are becoming obsolete because of the exponential growth of the social networks, in terms of the number of active users
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