Community detection in attributed collaboration network for statisticians
Yan Zhang, Rui Pan, Hansheng Wang, Haibo Su
Abstract
Scientific collaboration helps to promote the dissemination of knowledge and is essential in breeding innovation. Collaboration network analysis is a useful tool to study researchers' collaborations. In this work, we collect papers published between 2001 and 2018 in 43 statistical journals and investigate the collaborative trends and patterns. We find that more and more researchers take part in statistical research, and cooperation among them is strengthening. We further construct an attributed collaboration network and extract its core. Community detection is conducted on the core network by using the edge cross‐validation (ECV) method and the attributed network clustering algorithm (ANCA). In particular, we extend the ANCA to deal with networks having both categorical and continuous attributes. Influential researchers are identified in each community. Furthermore, two kinds of homophily are revealed in our collaboration network: research topic homophily and spatial proximity homophily. Based on the homophily and transitivity, we can make recommendations for researchers. Finally, we compare ANCA with the other three methods and confirm that the combination of nodal attributes and network structure improves the quality of community detection. Our studies show the features of the collaboration among statisticians and present a new perspective to explore researchers.