# Ranking Authors in an Academic Network Using Social Network Measures
## 1 Introduction  
In the present era, online social networks are widely-used platforms that enable people to connect with other people for exchanging ideas, views and setting public opinion. Types of online social networks include, but are not limited to, Facebook, Flicker, Twitter, YouTube, etc. According to the statistics, Facebook had about 2.2 billion monthly active users in March 2018. Similarly, the total number of monthly active users of Twitter in January 2018 was 330 million, who share about 500 million tweets per day. Flicker is used by 92 million people worldwide and is a great place to store and share photos online [1]. These online social networking sites provide an active communication platform for people, and they have opened new venues of research for the academic world and businesses. One of these research areas is finding the influential users in the online social networks. According to the related research studies [2; 3; 4; 5; 6], the influence of top users is measured with the help of various techniques, such as the feature-based technique or link-based technique. Moreover, in all these techniques, the focus has been to find the top active or influential users. Like online social networks, there are a number of online academic networks. These are also represented by the relationship among the entities or nodes, such as paper, author, journal or conference. These entities or nodes are connected through two types of relationships: co-citation and co-author, and result in two subtypes of academic network, which are the co-citation network and co-authorship network, respectively. In the citation network, one author cites the publication of other authors, whereas in the co-author network, links are formed between authors based on a publication. As in online social networks, finding influential authors in an academic network is an active research area as well, and the co-authorship network has been used to find the influential authors or the ranking of authors. The research domain of ranking of authors has vast applications such as finding the top supervisor in a specific research domain, offering research jobs, grant funding for research projects and nominating top authors for research awards [7]. In this regard, to rank the authors, using various indexing techniques, such as the h-index [8], g-index [9], m-index [10], R-index [11] and AR-index [11], are used. However, all these indexing techniques evaluate the author to only gauge the influence of authors, but rarely consider discipline or domain along with the impact of authors. To address this problem, centrality measure techniques from social network measures are borrowed. Moreover, authors have used PageRank [12] along with the centrality measure and compared the results with citation count. In another study [13], the authors used a technique from social network measures, i.e., Eigenvector to rank the journals of the PLOS database and compared the result with the citation count within a given journal. Moreover, the techniques borrowed from social network measures often focus on macro-level properties of the co-authorship network, yet no attention is paid to micro-level properties. In this article, we propose the adoption of established social network measures in order to compute the centrality and significance of an author. Our contributions may be summarized as follows:  
* We analyze the co-author network within the Digital Bibliography and Library Project (DBLP) research community at the macro-level by applying centrality measures (betweenness centrality, closeness centrality, degree centrality) and prestige measures (PageRank, Eigenvector) for author ranking. * We also study the micro-level network properties of the co-author network using the average path length, the largest connected component and the average degree of a network.