Comparative study on methods of detecting research fronts using different types of citation

Abstract

In this article, we performed a comparative study to investigate the performance of methods for detecting emerging research fronts. Three types of citation network, co-citation, bibliographic coupling, and direct citation, were tested in three research domains, gallium nitride (GaN), complex network (CNW), and carbon nanotube (CNT). Three types of citation network were constructed for each research domain, and the papers in those domains were divided into clusters to detect the research front. We evaluated the performance of each type of citation network in detecting a research front by using the following measures of papers in the cluster: visibility, measured by normalized cluster size, speed, measured by average publication year, and topological relevance, measured by density. Direct citation, which could detect large and young emerging clusters earlier, shows the best performance in detecting a research front, and co-citation shows the worst. Additionally, in direct citation networks, the clustering coefficient was the largest, which suggests that the content similarity of papers connected by direct citations is the greatest and that direct citation networks have the least risk of missing emerging research domains because core papers are included in the largest component.

The value of indirect ties in citation networks: SNA analysis with OWA operator weights

“…In Fig. 2, there is bibliographic coupling between F and G, as they both cite D. Recent studies have validated the performance of these methods to detect emerging research front [66,12] and investigated citation networks with combinational types of citations, such as including direct citations and co-citations to exploit new possibilities of detecting research fronts [32]. The concept of indirect citations proposed in this paper differs from these studies, as described in our example referring to Fig.…”

Scientometric Analysis of the Researches About Technological Pedagogical Content Knowledge and Scholarly Communication

“…Her iki veri tabanı da genel amaçlı bilimsel yayınların atıf istatistikleri ve yayınların bibliyografik verilerine erişim imkanı sağlamaktadır (Meho ve Yang, 2007). Atıf analizi yöntemi gereği veri seti içindeki atıf bilgilerinin bilimsel anlamda tutarlı ve geçerli olması analizin ön şartını oluşturmaktadır (Shibata, Kajikawa, Takeda ve Matsushima, 2009). Bu özellikler dikkate alındığında Google Akademik veri tabanı daha fazla yayına erişim imkanı sağlamasına rağmen tutarlılık ve güvenirlik kriterleri nedeniyle kapsam dışı bırakılmıştır (Mikki, 2009).…”

The extraction of community structures from publication networks to support ethnographic observations of field differences in scientific communication

“…The clustering of citation networks to study research specialties has been pioneered by Small (1973) based on a subset of highly cited documents and using co-citation instead of direct citations. Recently, Shibata et al (2009) conducted a comparison of direct citation, co-citation, and bibliographic coupling to detect research fronts, and concluded that direct citation outperformed the other two approaches in detecting research fronts as it identified large and emerging clusters earlier. In addition, they observed that direct citation networks had the largest clustering coefficients, an indication of higher content similarity.…”