A bibliometric approach to Systematic Mapping Studies: The case of the evolution and perspectives of community detection in complex networks

📝 Abstract

Critical analysis of the state of the art is a necessary task when identifying new research lines worthwhile to pursue. To such an end, all the available work related to the field of interest must be taken into account. The key point is how to organize, analyze, and make sense of the huge amount of scientific literature available today on any topic. To tackle this problem, we present here a bibliometric approach to Systematic Mapping Studies (SMS). Thus, a modify SMS protocol is used relying on the scientific references metadata to extract, process and interpret the wealth of information contained in nowadays research literature. As a test case, the procedure is applied to determine the current state and perspectives of community detection in complex networks. Our results show that community detection is a still active, far from exhausted, in development, field. In addition, we find that, by far, the most exploited methods are those related to determining hierarchical community structures. On the other hand, the results show that fuzzy clustering techniques, despite their interest, are underdeveloped as well as the adaptation of existing algorithms to parallel or, more specifically, distributed, computational systems.

💡 Analysis

Critical analysis of the state of the art is a necessary task when identifying new research lines worthwhile to pursue. To such an end, all the available work related to the field of interest must be taken into account. The key point is how to organize, analyze, and make sense of the huge amount of scientific literature available today on any topic. To tackle this problem, we present here a bibliometric approach to Systematic Mapping Studies (SMS). Thus, a modify SMS protocol is used relying on the scientific references metadata to extract, process and interpret the wealth of information contained in nowadays research literature. As a test case, the procedure is applied to determine the current state and perspectives of community detection in complex networks. Our results show that community detection is a still active, far from exhausted, in development, field. In addition, we find that, by far, the most exploited methods are those related to determining hierarchical community structures. On the other hand, the results show that fuzzy clustering techniques, despite their interest, are underdeveloped as well as the adaptation of existing algorithms to parallel or, more specifically, distributed, computational systems.

📄 Content

1    A bibliometric approach to Systematic Mapping Studies: The case of the evolution and perspectives of community detection in complex networks

Camelia Muñoz-Caro*. SciCom Research Group. Escuela Superior de Informática. Universidad de Castilla-La Mancha. Paseo de la Universidad 4, 13004 Ciudad Real, Spain. Email: camelia.munoz@uclm.es

Alfonso Niño. SciCom Research Group. Escuela Superior de Informática. Universidad de Castilla-La Mancha. Paseo de la Universidad 4, 13004 Ciudad Real, Spain. Email: alfonso.nino@uclm.es

Sebastián Reyes. SciCom Research Group. Escuela Superior de Informática. Universidad de Castilla-La Mancha. Paseo de la Universidad 4, 13004 Ciudad Real, Spain. Email: sebastian.reyes@uclm.es

*Corresponding author.

Acknowledgements The authors wish to thank the Consejería de Educación y Ciencia de la Junta de Comunidades de Castilla-La Mancha (grant # PEII-2014-020-A). The economic support of the Universidad de Castilla-La Mancha is also acknowledged.

2    Abstract Critical analysis of the state of the art is a necessary task when identifying new research lines worthwhile to pursue. To such an end, all the available work related to the field of interest must be taken into account. The key point is how to organize, analyze, and make sense of the huge amount of scientific literature available today on any topic. To tackle this problem, we present here a bibliometric approach to Systematic Mapping Studies (SMS). Thus, a modify SMS protocol is used relying on the scientific references metadata to extract, process and interpret the wealth of information contained in nowadays research literature. As a test case, the procedure is applied to determine the current state and perspectives of community detection in complex networks. Our results show that community detection is a still active, far from exhausted, in development, field. In addition, we find that, by far, the most exploited methods are those related to determining hierarchical community structures. On the other hand, the results show that fuzzy clustering techniques, despite their interest, are underdeveloped as well as the adaptation of existing algorithms to parallel or, more specifically, distributed, computational systems.

3    Introduction
New research lines worthwhile to pursue by the scientific and technological community must be properly oriented. To such an end, all the available work related to the field of interest must be taken into account. The main problem is how to organize, analyze, and make sense of the huge amount of scientific literature existing today on any topic. As an example of the difficulties faced when identifying a new research topic, we have the case of community detection in complex networks. The complex networks paradigm represents a powerful way to describe and handle complex systems. Under this paradigm, a system is described as a set of nodes, the entities of the system, interrelated by a series of edges representing the interactions among the entities. The interest in this approach was triggered by the seminal works of Watts and Strogatz on small-world networks (Watts & Strogatz, 1998), and Barabási and Albert on the generation of scale-free networks by preferential attachment (Barabási & Albert, 1999). Since then, the field has grown rapidly, being applied to the interpretation and prediction of the behavior of communication, social, biological, epidemiological, chemical or transportation networks (Costa et al., 2011).
Many different studies can be carried out on networks (Newman, 2010; Barabási, 2016), but one of the most interesting is community detection. Community structure, or clustering, is a consequence of inhomogeneities in the network. Thus, some nodes are more “strongly” connected among them than with others. This fact defines communities of nodes (Fortunato, 2010) with probably similar state or behavior. The problem is how to characterize this “strength” of the connection. No single approach exists; it usually depends on the problem considered, although it is frequently related to edge density (Fortunato, 2010). Therefore, many different types of methods exist such as hierarchical, partitional or spectral algorithms, among others (Schaeffer, 2007; Fortunato, 2010; Coscia et al., 2012; Xie et al., 2013; Barabási, 2016). Community detection in complex networks presents a great interest due to the huge number of practical applications. The key point is that nodes in the same community represent entities with similar characteristics. So, in a social network, communities represent interrelated individuals such as families, friend circles, or co-workers. In communications networks, they correspond to systems strongly interrelated where ease of communication would represent an increase of the total efficiency of the system. In a sales system, communities can represent sets of customers with similar preferenc

This content is AI-processed based on ArXiv data.