基于ORCID和加权跨层边聚类系数的研究者社区发现

Community Detection of Researchers Based on ORCID and Weighted Cross-Layer Edge Clustering Coefficients

在开放学术的环境下,学术交流和科研合作在学术创新与发展中发挥着重要的作用,而帮助研究者找到合适的学术团体是促进研究者寻找科研灵感的重要途径,现有的研究者社区发现多是着眼于科研成果的关联而忽略了研究者自身学术活动产生的关联,因此,本文通过分析研究者自身学术活动信息,使用ORCID (Open Research and Contributor ID,开放研究者与贡献者标识)数据构建学术信息网络,通过综合考虑所有层次数下节点间的相似度来改进跨层边聚类系数,提出一种基于加权跨层边聚类系数的研究者社区发现模型.模型通过构建多种元路径抽取研究者之间的直接关联关系,并根据不同属性关系对网络进行分层,使用加权跨层边聚类系数计算节点间相似度,从而将网络转化为同质网络并结合Louvain算法进行社区划分.本文在人造网络和真实网络中进行实验,根据社区实际情况对结果进行评估,在提高了划分效果的同时避免了参数的不确定性.

In an open academic environment,academic exchanges and scientific research cooperation are pivotal to academic innovation and development,and helping researchers find suitable academic groups contributes greatly to their inspiration.Most of the existing approaches to community detection of researchers focus on the correlation between research results and ignore that between their own academic activities.As such,this study uses Open Research and Contributor ID(ORCID)data to build a network of academic information by analyzing researchers’academic activities.The cross-layer edge clustering coefficient is improved on the basis of similarity between nodes at all levels,and then a model detecting the researcher community based on the weighted cross-layer edge clustering coefficient is proposed.The model extracts the direct correlations between researchers by constructing multiple meta-paths and stratifies the network according to different attribute relationships.Inter-node similarity is calculated with weighted cross-layer edge clustering coefficients.Then the network is transformed into a homogeneous network which is combined with the Louvain algorithm for community detection.Experiments are carried out in both artificial and real networks,and the results are evaluated according to the actual situation of the community,improving the division while avoiding the uncertainty of parameters.