WenJun Zhang School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China; International Academy of Ecology and Environmental Sciences, Hong Kong Network Biology ISSN 2220-8879 http://www.iaees.org/publications/journals/nb/online-version.asp 2027 17 2 468 537 International Academy of Ecology and Environmental Sciences Hong Kong 30 June 2026 12 July 2026 1 June 2027 network visualization force-directed layout graph drawing algorithm complex network analysis interactive visualization browser-based application data format parsing Complex network visualization plays a crucial role in network science research, facilitating the understanding of topological structures and the discovery of underlying patterns. This paper presents the design and implementation of a browser-based interactive network visualization tool, aiming to provide researchers with a convenient and efficient platform for network analysis and presentation. The proposed tool utilizes HTML5 Canvas technology for graphical rendering and supports multiple common data formats including TXT, CSV, XLS, XLSX, and JSON. A key feature is its ability to automatically distinguish between weighted networks (four-column data) and unweighted networks (three-column data), enabling intelligent data parsing without manual configuration. The system incorporates six classical layout algorithms: force-directed layout, circular layout, hierarchical layout, grid layout, radial layout, and spectral layout, allowing users to select the optimal visualization scheme based on specific network characteristics. Regarding interaction design, the tool provides node dragging, hover information tooltips, and real-time parameter adjustment capabilities. It supports the visualization of both directed and undirected edges, with edge thickness representing weight information intuitively. Additionally, the system offers image export functionality, enabling users to save visualization results in PNG format for academic publications. The proposed tool will effectively handle small to medium-scale network data with stable algorithm performance and clear visualization output, exhibiting practical value and promising application prospects. http://www.iaees.org/publications/journals/nb/articles/2027-17(2)/NetGen4.0.pdf