Visualizing Fuzzy Relationship in Bibliographic Big Data Using Hybrid Approach Combining Fuzzyc-Means and Newman-Girvan Algorithm

Abstract

Bibliographic big data visualization method is proposed by incorporating a combination of fuzzyc-means clustering and the Newman-Girvan clustering algorithm, where clustered results are displayed in a network view by grouping objects with similar cluster memberships. As current bibliographic visualizations focus on the crisp relationship among data, fuzzy analysis and visualization may offer insights to bibliographic big data, enabling faster decision making by improving displayed information precision. The proposed method is applied to the DBLP citation network dataset. Results show that merging two clustering algorithms and visualization using fuzzy techniques enables the user to converge a few target papers within an average of 5 minutes from 1.5 million papers stored in the DBLP. Users targeted for the proposed method include researchers, educators, and students who hope to use real-world social and biological networks. The proposal is planned to be opened to the public through the Internet.

Inferring the densest multi-profiled cross-community for a user

“…In this evaluation, we used the DBLP Citation Network Dataset (DBLP-Citation-network V11) [53,54,55] compiled by the AMiner.org project (http://www.arnetminer.org/developer#introduction). The original data was extracted from the DBLP compiled by the Stanford Network Analysis Project (SNAP) [56].…”

Dynamic Force-directed Graph with Weighted Nodes for Scholar Network Visualization

“…5 shows the visualizing patterns and trends visualised in the scientific literature using CiteSpace. A fuzzy-based clustering visualization approach, Bibliographic Big Data Visualization [12] offers a hybrid fuzzy clustering-based visualization by applying the Fruchterman-Reingold algorithm. The visualization can divide the nodes into soft clusters, but they lack the strength of the connection between the nodes.…”