Using Machine Learning to Predict the Evolution of Physics Research

doi:doi:10.57702/vf6wgamm

Using Machine Learning to Predict the Evolution of Physics Research

The APS publication data set from 1981 to 2010. The authors analyze the APS publication data set from 1981 to 2010. They build the bibliographic coupling and co-citation networks, use the Louvain method to detect topical clusters (TCs) in each year, measure the similarity of TCs in consecutive years, and visualize the results as alluvial diagrams.

BibTex:

@dataset{Wenyuan_Liu_and_Stanisław_Saganowski_and_Przemysław_Kazienko_and_Siew_Ann_Cheong_2024,
    abstract = {The APS publication data set from 1981 to 2010. The authors analyze the APS publication data set from 1981 to 2010. They build the bibliographic coupling and co-citation networks, use the Louvain method to detect topical clusters (TCs) in each year, measure the similarity of TCs in consecutive years, and visualize the results as alluvial diagrams.},
    author = {Wenyuan Liu and Stanisław Saganowski and Przemysław Kazienko and Siew Ann Cheong},
    doi = {10.57702/vf6wgamm},
    institution = {No Organization},
    keyword = {'alluvial diagrams', 'bibliographic coupling', 'co-citation networks', 'physics research evolution', 'topical clusters'},
    month = {dec},
    publisher = {TIB},
    title = {Using Machine Learning to Predict the Evolution of Physics Research},
    url = {https://service.tib.eu/ldmservice/dataset/using-machine-learning-to-predict-the-evolution-of-physics-research},
    year = {2024}
}