Public opinion bunching storage model for dense graph data in social networks¹

Article type: Research Article

Authors: Cui, Wanqiu^{; *}

Affiliations: School of National Security, People’s Public Security University of China, Beijing, China

Correspondence: [*] Corresponding author. Wanqiu Cui. E-mail: [email protected].

Note: [1] This work was supported by the Double First-Class Innovation Research Project for People’s Public Security University of China (No. 2023SYL20).

Abstract: Graph data storage has a promising prospect due to the surge of graph-structure data. Especially in social networks, it is widely used because hot public opinions trigger some network structures consisting of massively associated entities. However, the current storage model suffers from slow processing speed in this dense association graph data. Thus, we propose a new storage model for dense graph data in social networks to improve data processing efficiency. First, we identify the public opinion network formed by hot topics or events. Second, we design the germ elements and public opinion bunching mapping relationship based on equivalence partition. Finally, the Public Opinion Bunching Storage(POBS) model is constructed to implement dense graph data storage effectively. Extensive experiments on Twitter datasets demonstrate that the proposed POBS performs favorably against the state-of-the-art graph data models for storage and processing.

Keywords: Graph data storage, social networks, topic cluster, equivalent partition

DOI: 10.3233/JIFS-233540

Journal: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9807-9818, 2024