Knowledge Discovery of Complex Networks Research Literatures

Fei-Cheng Ma, Peng-Hui Lyu and Xiao-Guang Wang

Abstract Complex network research literatures have increased rapidly over last decade, most remarkable in the past four years. This paper attempted to visualise the research outputs of complex network research in a global context for the purpose in knowledge discovery on the world research progress and quantitative analysing on current research publication trends. The scientometric methods and knowledge visualization technologies are employed with a focus on global production, main subject categories, core journals, the most productive countries, leading research institutes, publications' most used keywords as well as the most cited papers, and the knowledge basement. The keywords cluster analysis is used to trace the hot topics from the research literatures in this field. Research outputs descriptors suggested that the research in this domain has mainly focused on the dynamics, model and systems for complex networks. All the publications have been concentrated in two journals such as Physical Review E and Physica A. The USA is the leading country in complex network research field since it has both the world research centres and most of the top scientists worldwide. The research trend in complex network research are involved in complex routing strategy, models complex networks social as well as scale free percolation efficiency. Complex networks, dynamics, model and small-world networks are highly used keywords in the literatures from the main scientific database.

Keywords Complex Networks Knowledge Discovery Publication Trend Citation

Analysis Knowledge Base Subjects Category Keywords Plus Co-Citation


Complex networks, attracting the attention of computer scientists, biologists, mathematicians and physicists, are thoroughly studied in more and more evolved research fields now. As an effective reflection contacting the real world and theoretical exploration, it was initially come from the domain of chaos theory and fractal studies. Two pioneering works, small world network and scale-free network, encouraged an instantly wave of international research concerning complex networks by the end of the twentieth century. Small-world networks explored by Watts and Strogatz, which can be highly clustered and have small characteristic path lengths (Watts and Strogatz 1998), can portray biological, technological and social networks better than the networks completely regular or completely random. In many large networks it was found that the property that the vertex connectivity followed a scale-free powerlaw distribution (Barabasi and Albert 1999) by Barabási A.L and Albert R. Counting from this emergence, complex networks have gone through its first research decade. In the early twenty-first century, the discovery of small world effect and scalefree property in the real network largely provoked the publications boom of complex networks. Initial research on complex networks focused on the analysis and modelling of network structure at large, such as degree exponents (Dorogovtsev and Goltsev 2002), dynamical processes (Yang et al. 2008), network growth (Gagen and Mattick 2005), link prediction (Zhou et al. 2009) and so on. Then Strogatz S.H tried to unravel the structure and dynamics of complex networks from the perspective of nonlinear dynamics (Strogatz 2001). The statistical mechanics of network as topology and dynamics of the main models as well as analytical tools were discussed (Albert and Barabasi 2002), the theory of evolving networks was introduced in Albert R and Barabasi A.L's work.

The developments of complex networks, including several major concepts, models of network growth, as well as dynamical processes (Newman 2003) were discussed in Newman MEJ's paper. The basic concepts as well as the results achieved in the study of the structure and dynamics of complex networks (Boccaletti et al. 2006) were summarized. The error tolerance was displayed only in scale-free networks, and it showed an unexpected degree of robustness (Albert et al. 2000). Network motifs and patterns of interconnections to uncover the structural design principles of complex networks was defined (Milo et al. 2002). The way in which self-organized networks grows into scale-free structures, and the role of the mechanism of preferential linking were investigated (Dorogovtsev and Mendes 2002). A number of models demonstrating the main features of evolving networks were also presented. Mixing patterns in a variety of networks were measured (Newman 2003) and technological as well as biological networks were found disproportionally mixed, while social networks tend to be assorted. It was pointed out that scale-free networks catalysed the emergence of network science (Barabasi and Oltvai 2004). The number of driver nodes is determined primarily by the network's degree distribution was also found, and the driver nodes tend to avoid the high-degree nodes (Liu et al. 2011). The control of degrees on complex networks was carefully studied later (Egerstedt 2011). The fragility of interdependency on complex networks was also studied hence (Vespignani 2010). With the continuous development of complex networks, in addition to the theoretical and technical research on the complex network itself, scholars have also focused on the network function. Barabasi A.L and Oltvai Z.N indicated that cellular networks offer a new conceptual framework for biology and disease pathologies (Barabasi 2009), which could potentially revolutionize the traditional view. An approach which not only stresses the systemic complexity of economic networks was pointed out (Schweitzer et al. 2009), it can be used to revise and extend traditional paradigms in economic theory which is urgently needed. A biologically complex multistring network model was designed to observe the evolution and transmission dynamics of ARV resistance (Smith et al. 2010).

The current situation is that the complex network research was not only limited to the study of the theory and methods, but has become a new research direction of multi-disciplinary and a powerful tool in multi-disciplinary research. Nowadays complex network have been applied in many different areas including spread (Yang et al. 2008), network synchronization (Motter et al. 2005), transports (Wang et al. 2006), game theory (Perc and Szolnoki 2010), physics (Newman 2002), computer science (Guimera and Amaral 2005), biochemistry or molecular biology (Jeong et al. 2000), mathematics (Guimera and Amaral 2005), engineering (Olfati-Saber et al. 2007), cell biology (Rosen and MacDougald 2006). These research directions took us more and more productions and publications in recent years.

Most important it was known to all that the methods of complex networks are used more and more for scientomtrics and informetrics research in information science. For example the complex networks analysis was employed for co-citation or co-occurrence network to get the knowledge structure as well as scientific cooperation performance for a specific filed. While in these studies, the metric data is the base of all complex networks analysis. Traditional bibliometrics research was widely applied to acquaint information from the scientific or technical literatures, and for further study the complex networks method could also help.

In this study the records of literature were analysed with scientometric methods via several aspects. This effort will provide a current view of the mainstream research on complex networks as well as clues to the impact of this hot topic.

In addition, this study also attempted to analyse the significance of the complex networks production patterns, especially in the way of co-authors and authors' keywords study originally acted from WoS database. The main body of this article includes scientometric analyses in production, subject category, and geographical distribution of WoS data. Moreover, appropriate statistical tests were used in the authors' keyword yearly to predict the developing trend of complex networks research.

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