University Research Graph Database For Efficient Multi-Perspective Data Analysis Using Neo4j

Afandi, Mohamad Irwan; Wahyuni, Eka Dyar

doi:10.1109/itis50118.2020.9320965

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…Citation networks implemented using relational data models can be more effective for giving answers to simple queries. However, when users request a more in-depth, more expansive, multi-perspective, and sometimes more abstract analysis, the relational database struggles to provide answers [1]. Relational data models are structured databases that store information in tables with predefined relationships between them.…”

Section: Introductionmentioning

confidence: 99%

Exploring Citation and Author Collaboration Network with Graph Database

Yadav,

Bhandari,

Nikam

2024

Preprint

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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.

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Section: Introductionmentioning

confidence: 99%