Exploring Citation and Author Collaboration Network with Graph Database

Abstract

There are several research papers available online that span a wide range of disciplines. Researchers are using this data through search engines, digital libraries and professional networks. To make effective use of this data we propose building a citation network consisting of papers, authors and citations. Complex queries based on connections can be best handled by graph databases like Neo4j. Neo4j is a graph database that is specially designed for interconnected data. Neo4j represents data in the form of nodes and relationships and uses Cypher query language for CRUD (CREATE, READ, UPDATE and DELETE) operations. The proposed study, uses DBLP citation dataset to model citation and author collaboration networks using Neo4j. The expressiveness of Cypher query language in extracting insights from vast bibliographic data has been explored. The citation and author collaboration networks are analyzed to identify influential papers, influential authors, co-author networks, and to compute researcher’s impact metrics.