学术社交媒体视角下学科知识流动规律研究——以科学网为例

Analysis of Knowledge Flow Based on Academic Social Networks:A Case Study of ScienceNet.cn

【目的】探索学术社交媒体上学科知识流动规律。【方法】以科学网为例,爬取全部用户研究方向数据和全体用户好友关系数据,利用简单相关系数分析学部用户知识流动分布关系的强弱,借用Louvain社区发现算法挖掘学部内一级学科知识流动中的社区结构。【结果】简单相关系数结果显示,不同学部之间知识交流分布相似程度较高, Louvain算法挖掘出4个明显的知识流动社区。【局限】仅仅依据好友关系构建学科知识流动网络,没有考虑评论及推荐关系。【结论】在科学网上,"生命科学"与"医学科学"表现出最明显的学科亲缘性,学科交流中存在"地球科学–生命学科–医学科学"、"化学科学–工程材料–数理科学–信息科学"、"地球科学–工程材料"、"信息科学–管理综合"这4个明显的知识流动路径。

[Objective] This study aims to explore the knowledge flow on academic social networks.[Methods] Take ScienceNet.cn as the representative,we first collect all the data about users' research directions and friends.Then,we use the simple correlation coefficient to measure the distribution relation of knowledge flow of different disciplines users,and adopt Louvain algorithm to detect the community structure among first-level disciplines.[Results] It is found that the knowledge flow of different disciplines is similar to each other through simple correlation coefficient.There are four knowledge-flow communities among first-level disciplines detected by Louvain algorithm.[Limitations] We construct knowledge flow network only based on friends' relationship,without considering comments and recommendation relationship.[Conclusions] Through our research,we find that "Life Science" and "Medical Science" showed the most obvious disciplinary affinity in ScienceNet.cn.In addition,there are four main knowledge flow paths cross discipline departments,such as "Earth Science-Life Science-Medical Science","Chemical Science-Engineering Material-Mathematical Science-Information Science",“Earth Science-Engineering Materials","Information Science-Management Science".