Article: Modelling news dissemination networks using a community-based graph traversal algorithm and its performance evaluation Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.65 pp.1 - 25 Abstract: In this study, a directed weighted graph containing 500,000 user nodes and 100,000 news records was constructed. Node attributes are labelled with user activity and news sentiment tendency. Edge weights were determined according to propagation time attenuation (5%/hour) and interaction frequency. The community was divided by Louvain's algorithm, and the core nodes were identified by fusing node betweenness centrality and PageRank. Path traversal was optimised with 0.2 restart probability. Compared with traditional methods such as the shortest path algorithm and static community random walk, CGTA achieves 92.3% (76.6% for traditional methods) and 89.1% for core node recall (71.4% for traditional methods), which are 15.7% and 17.7% higher respectively. The structural equation model quantified that user activity (38.2%), emotional tendency (29.5%), and propagation time (22.3%) dominated the path formation, and the propagation speed decreased by 12.3% (r = -0.78) for every 1 hop increase in path length, and the core node expansion effect reached 67.5%. The study integrates multi-source data from Weibo and Toutiao, and cross-validates it with 5% to confirm the effectiveness of the algorithm in breaking/regular news and communities of different sizes. Inderscience Publishers - linking academia, business and industry through research

Title: Modelling news dissemination networks using a community-based graph traversal algorithm and its performance evaluation

Authors: Wei Ren; Yu Wang

Addresses: School of Culture and Communication, Shenyang City University, Liaoning 110000, China ' Neusoft Corporation, Neusoft Software Park, No. 2 Xinxiu Street, Hunnan District, Shenyang City, Liaoning Province, 110179, China

Abstract: In this study, a directed weighted graph containing 500,000 user nodes and 100,000 news records was constructed. Node attributes are labelled with user activity and news sentiment tendency. Edge weights were determined according to propagation time attenuation (5%/hour) and interaction frequency. The community was divided by Louvain's algorithm, and the core nodes were identified by fusing node betweenness centrality and PageRank. Path traversal was optimised with 0.2 restart probability. Compared with traditional methods such as the shortest path algorithm and static community random walk, CGTA achieves 92.3% (76.6% for traditional methods) and 89.1% for core node recall (71.4% for traditional methods), which are 15.7% and 17.7% higher respectively. The structural equation model quantified that user activity (38.2%), emotional tendency (29.5%), and propagation time (22.3%) dominated the path formation, and the propagation speed decreased by 12.3% (r = -0.78) for every 1 hop increase in path length, and the core node expansion effect reached 67.5%. The study integrates multi-source data from Weibo and Toutiao, and cross-validates it with 5% to confirm the effectiveness of the algorithm in breaking/regular news and communities of different sizes.

Keywords: CGTA algorithm; news communication path; core node; communication influence factors.

DOI: 10.1504/IJICT.2026.154186

International Journal of Information and Communication Technology, 2026 Vol.27 No.65, pp.1 - 25

Received: 19 Nov 2025
Accepted: 07 Jan 2026
Published online: 15 Jun 2026 *

Title: Modelling news dissemination networks using a community-based graph traversal algorithm and its performance evaluation

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