书目耦合
计算机科学
相似性(几何)
引用
共引
多维标度
余弦相似度
中心性
复杂网络
数据科学
情报检索
人工智能
万维网
机器学习
数学
聚类分析
图像(数学)
组合数学
摘要
This study explores the similarity among six types of scholarly networks aggregated at the institution level, including bibliographic coupling networks, citation networks, cocitation networks, topical networks, coauthorship networks, and coword networks. Cosine distance is chosen to measure the similarities among the six networks. The authors found that topical networks and coauthorship networks have the lowest similarity; cocitation networks and citation networks have high similarity; bibliographic coupling networks and cocitation networks have high similarity; and coword networks and topical networks have high similarity. In addition, through multidimensional scaling, two dimensions can be identified among the six networks: D imension 1 can be interpreted as citation‐based versus noncitation‐based, and D imension 2 can be interpreted as social versus cognitive. The authors recommend the use of hybrid or heterogeneous networks to study research interaction and scholarly communications.
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