Investigating the Impact of Authors’ Rank in Bibliographic Networks on Expertise Retrieval

Background and Aim: this research investigates the impact of authors’ rank in Bibliographic networks on document-centered model of Expertise Retrieval. Its purpose is to find out what kind of authors’ ranking in bibliographic networks can improve the performance of document-centered model.   Methodology: Current research is an experimental one. To operationalize research goals, a new test collection was developed which includes 55 queries and 96375 documents. The queries were made by Iran Knowledge and Information Science PhD students, and the documents were papers indexed in the Web of Science database under Library Science and Information Science category. The queries were submitted to the database consisting of test collection documents, and then DLH13, a known IR model, were used to retrieve documents from database. The first 100 documents retrieved by DLH13 model for each query were chosen for second stage. All people names occurred in the retrieved documents were extracted, processed, and ranked in 5 different ways based on micro metrics of Social Network Analysis. The top 10 results of every method accumulated in a pool of authors. After relevance judgment on authors’ expertise, the expert finding performance of every ranking method was measured. Findings: Results showed that performance of authors’ ranking in citation networks hadn’t significant difference with document-centered model, whereas authors’ ranking in co-authorship networks was weaker than document-centered model, and impact it negatively.    Conclusion: compared with author-based networks, citation-based networks are better evidence for individual’s expertise in different subject areas.