Impact of mutual influence while ranking authors in a co-authorship network (original) (raw)

Ranking Authors in an Academic Network Using Social Network Measures

Applied Sciences

Online social networks are widely used platforms that enable people to connect with each other. These social media channels provide an active communication platform for people, and they have opened new venues of research for the academic world and business. One of these research areas is measuring the influential users in online social networks; and the same is true for academic networks where finding influential authors is an area of interest. In an academic network, citation count, h-index and their variations are used to find top authors. In this article, we propose the adoption of established social network measures, including centrality and prestige, in an academic network to compute the rank of authors. For the empirical analysis, the widely-used dataset of the Digital Bibliography and Library Project (DBLP) is exploited in this research, and the micro-level properties of the network formed in the DBLP co-authorship network are studied. Afterwards, the results are computed usi...

A novel approach to Rank Authors in an Academic Network

Ranking the authors in an academic network is a significant research domain to find the top authors in various domains. We find various links based ranking algorithms and index based approaches to measure the productivity and impact of an author in a social network of authors. The research problem to rank the experts has vast applications such as advisor finding, domain expert identification. In this paper, we propose a novel approach to rank the scholars in the academic network of DBLP, a well-known computer science bibliography website. A huge data set is prepared covering the publications of more than 70 years. We propose AuthorRank and Weighted AuthorRank algorithms based on the state of the art ranking algorithms of PageRank and weighted PageRank algorithms respectively. For weighted algorithms, existing methods lack to provide diverse weights. We introduce the novel weights of h-index, g-index and R-index and elaborate their impact to identify the top authors in the scholarly network. The results confirm that the proposed algorithms find the top authors in an effective manner.

Discovering author impact: A PageRank perspective

2011

This article provides an alternative perspective for measuring author impact by applying PageRank algorithm to a coauthorship network. A weighted PageRank algorithm considering citation and coauthorship network topology is proposed. We test this algorithm under different damping factors by evaluating author impact in the informetrics research community.

Finding Rising Stars in Co-Author Networks via Weighted Mutual Influence

Finding rising stars is a challenging and interesting task which is being investigated recently in co-author networks. Rising stars are authors who have a low research profile in the start of their career but may become experts in the future. This paper introduces a new method Weighted Mutual Influence Rank (WMIRank) for finding rising stars. WMIRank exploits influence of co-authors' citations, order of appearance and publication venues. Comprehensive experiments are performed to analyze the performance of WMIRank in comparison to baseline methods, which have ignored weighted mutual influence. AMiner 1 data for years 1995-2000 is used for experiments. List of top 30 authors as per proposed and baseline methods are compared for their average number of papers, average number of citations and achievements. Experimental results provide convincing evidence of the effectiveness of the investigated weighted mutual influence.