DICO: A Graph-DB Framework for Community Detection on Big Scholarly Data - Mercorio, Fabio (IEEE Xplore, 2020)
The widespread use of Online Social Networks has also involved the scientific field in which researchers interact each other by publishing or citing a given paper. The huge amount of information about scientific research documents has been described through the term Big Scholarly Data. In this paper we propose a framework, namely Discovery Information using COmmunity detection (DICO), for identifying overlapped communities of authors from Big Scholarly Data by modeling authors’ interactions through a novel graph-based data model combining jointly document metadata with semantic information. In particular, DICO presents three distinctive characteristics: i) the co-authorship network has been built from publication records using a novel approach for estimating relationships weight between users; ii) a new community detection algorithm based on Node Location Analysis has been developed to identify overlapped communities; iii) some built-in queries are provided to browse the generated network, though any graph-traversal query can be implemented through the Cypher query language. The experimental evaluation has been carried out to evaluate the efficacy of the proposed community detection algorithm on benchmark networks. Finally, DICO has been tested on a real-world Big Scholarly Dataset to show its usefulness working on the DBLP+AMiner dataset, that contains 1.7M+ distinct authors, 3M+ papers, handling 25M+ citation relationships.