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  "author": "Wenyuan Liu",
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  "author": "Wenyuan Liu",
            
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  "extra_authors": [
            
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    {
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    {
            
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      "extra_author": "Stanis\u0142aw Saganowski",
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      "extra_author": "Stanis\u0142aw Saganowski",
            
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    {
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    {
            
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      "extra_author": "Przemys\u0142aw Kazienko",
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      "extra_author": "Przemys\u0142aw Kazienko",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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      "extra_author": "Siew Ann Cheong",
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      "extra_author": "Siew Ann Cheong",
            
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      "orcid": ""
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      "description": "",
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      "description": "",
            
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      "name": "scientific-knowledge-evolution",
            
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      "title": "Scientific Knowledge Evolution"
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      "title": "Scientific Knowledge Evolution"
            
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  "name": 
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  "name": 
            
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"using-machine-learning-to-predict-the-evolution-of-physics-research",
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"using-machine-learning-to-predict-the-evolution-of-physics-research",
            
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  "notes": "The APS publication data set from 1981 to 2010. The 
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  "notes": "The APS publication data set from 1981 to 2010. The 
            
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authors analyze the APS publication data set from 1981 to 2010. They 
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authors analyze the APS publication data set from 1981 to 2010. They 
            
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build the bibliographic coupling and co-citation networks, use the 
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build the bibliographic coupling and co-citation networks, use the 
            
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Louvain method to detect topical clusters (TCs) in each year, measure 
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Louvain method to detect topical clusters (TCs) in each year, measure 
            
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the similarity of TCs in consecutive years, and visualize the results 
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the similarity of TCs in consecutive years, and visualize the results 
            
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as alluvial diagrams.",
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as alluvial diagrams.",
            
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  ],
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  "title": "Using Machine Learning to Predict the Evolution of Physics 
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  "title": "Using Machine Learning to Predict the Evolution of Physics 
            
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Research",
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Research",
            
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  "version": ""
            
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