Abstract

The collaboration networks are classified and studied by analysing various properties of the collaboration data of contributing scholars, and the modules with high information flow are identified for identifying the collaboration patterns of authors in science and technology disciplines. The basic model of the collaboration network is described in terms of statistical parameters such as network degree distribution, clustering coefficients, efficiency and average network path length, and then more efficient core communities of the original network are visualized and analysed after k-core decomposition and modularly identified.The present study shows that authors contributing to the field of general relativity and quantum cosmology tend to cooperate with the same fraction of the population, which results in information transfer efficiency in and between core communities being much greater than the overall network efficiency. In addition, there are also individual authors who prefer to do their research independently.

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