We explore classifying scientific disciplines including their temporal features by focusing on their collaboration structures over time. Bibliometric data for Slovenian researchers registered at the Slovenian Research Agency were used. These data were obtained from the Slovenian National Current Research Information System. We applied a recently developed hierarchical clustering procedure for symbolic data to the coauthorship structure of scientific disciplines. To track temporal changes, we divided data for the period 1986–2010 into five 5-year time periods. The clusters of disciplines for the Slovene science system revealed 5 clusters of scientific disciplines that, in large measure, correspond with the official national classification of sciences. However, there were also some significant differences pointing to the need for a dynamic classification system of sciences to better characterize them. Implications stemming from these results, especially with regard to classifying scientific disciplines, understanding the collaborative structure of science, and research and development policies, are discussed.
COBISS.SI-ID: 32693853
The authors of this book explore social mechanisms that drive network change and link them to computationally sound models of changing structure to detect patterns. This text identifies the social processes generating these networks and how networks have evolved. The authors explore three types of citation networks (bibliometrics, patent citations and citations across Supreme Court cases), networks of football player movements to the EPL and spatial network of 3111 counties in the continental United States.
COBISS.SI-ID: 31563993
Two clustering algorithms based on modularity – the VOS and Louvain methods – were applied for updating and optimizing the journal classification of the SCImago Journal & Country Rank (SJR) platform. We used network analysis program Pajek to run both algorithms on a network of more than 18,000 SJR journals combining three citation-based measures of direct citation, co-citation and bibliographic coupling. The set of clusters obtained was termed through category labels assigned to SJR journals and significant words from journal titles. The two newly generated algorithm-based classifications were compared to other bibliometric classification systems, including the original SJR and WoS Subject Categories, in order to validate their consistency, adequacy and accuracy. In addition to some noteworthy differences, we found a certain coherence and homogeneity among the four classification systems analysed.
COBISS.SI-ID: 17105241