药物领域三元闭包形成的影响机制研究——基于机会-信任-动机视角

Influencing Mechanisms of Triadic Closure in Pharmaceutical—Opportunity,Trust,and Motivation

【目的】基于社交有向网络中影响三元闭包形成的机会、信任、动机三机制视角,深入探讨药物领域三元闭包形成的影响机制,为药物知识发现提供基础研究。【方法】借助社会网络指标测度机会、信任和动机三类机制,利用皮尔逊法检验三类机制与三元闭包边聚类系数及三元闭包数量的相关关系;并引入更多节点属性和网络特征,通过计量经济学的方法深入检验节点属性与网络特征对三类机制的影响作用。【结果】节点对机会与节点对边聚类系数呈强正相关性(r_(1)>0.5);节点对信任、动机与节点对所在封闭三元组数目呈强正相关性(r_(3)、r5>0.5);节点对邻近中心性对机会、信任具有负向影响作用,对动机具有正向影响作用;节点对中介中心性与特征向量中心性对机会、信任、动机均具有正向影响作用;网络平均路径长度对节点对机会具有负向影响作用,对节点对信任、动机具有正向影响作用。【局限】选用的主题数据规模较小,未纳入大规模文献进行实证分析。【结论】提出的药物领域三元闭包形成三种影响机制均能较好地表现节点对三元闭包形成情况,并发现节点属性与网络特征对三机制具有影响作用,可为药物知识发现提供新的探索角度。

[Objective]Opportunity,trust,and motivation are the three mechanisms influencing the formation of triadic closure in directed social networks.This paper explores the mechanisms affecting triadic closure in the pharmaceutical domain,aiming to provide foundations for drug knowledge discovery.[Methods]First,we used social network indices to measure the three mechanisms of opportunity,trust,and motivation.Then,we examined the Pearson correlation coefficient between these mechanisms and the triadic closure clustering and their numbers.Third,we introduced additional node attributes,network characteristics,and econometric methods to examine the relationships between node attributes and network characteristics/the three mechanisms.[Results]The node pairs for the opportunity and the clustering coefficient of the edges between them showed a strong positive correlation(r_(1)>0.5).The node pairs for trust and motivation showed a strong positive correlation with the number of enclosed triads they belong to(r_(3),r5>0.5).The closeness centrality of node pairs negatively impacted the opportunity and trust,but a positive impact on motivation.The betweenness centrality and eigenvector centrality of node pairs positively impacted opportunity,trust,and motivation.The average path length of the network negatively affected the opportunity of node pairs but positively impacted their trust and motivation.[Limitations]More literature needs to be included for empirical analysis in the future.[Conclusions]The proposed method illustrates the circumstances of triadic closure formation for node pairs.Node attributes and network characteristics influence the three mechanisms,which provides a new direction for drug knowledge discovery.