Abstract: Identification of the local structure of scientists’ collaboration network can help understand the cooperative behavior of scientists. Based on complex network subgraph and social network structural equivalence, the subgraph structural equivalence based role identification method was proposed, and applied to the role analysis of the collaboration network involving scientists from complex network research. Using 11 kinds of node-positions in the six 4-node subgraphs, the node-position properties of the scientists’ collaboration network were investigated. It was found that the 11 kinds of node-positions can be divided into three categories of roles, i.e., core, intermediary, and edge. The diversity of the roles of scientists was also found, and the number of role types was positively correlated with degree, betweenness and closeness centrality measures. The top 20 scientists (degree ranked) were divided into two subgroups, based on the distributions of the relative number of roles. The study provides a quantitative method for role analysis of individual actors in social cooperation networks.

摘要： 辨识科学家合作网络的局部结构可以增进对科学家合作行为的理解。基于复杂网络子图搜索和社会网络结构对等性，提出基于子图结构对等的科学家合作网络角色辨识方法，并应用于复杂网络领域科学家合作网络角色分析中。研究界定了六种4节点子图的11种点位，讨论了复杂网络领域科学家合作网络点位特征，并依据科学合作特性将11种点位分为核心、中介和边缘三类角色；分析发现科学家角色种类的多样性，且其种类数与度、介数和接近性等中心性指标呈正相关关系；基于角色相对数量分布将度排名前20位的顶尖科学家分为两个子群体。研究提供了社会合作网络个体行动者角色辨识的定量方法。

关键词: 复杂网络, 社会网络, 科学家合作网络, 子图结构对等, 角色, Complex network, Social network, Scientists’ collaboration network, Subgraph structural equivalence, Role