Real-Time Network Monitoring Dashboard
BackA real-time network monitoring and visualization dashboard built with Python, Streamlit, NetworkX, and Plotly. The application simulates evolving graph data, calculates live network analytics such as density, clustering, centrality, modularity, and connectivity, and presents them through an interactive dashboard with filtering, layout controls, update history, and time-based network evolution tracking.
Project Overview
Real-Time Network Monitoring Dashboard is an interactive analytics tool designed to visualize and monitor dynamic network structures as they evolve over time. Built with Streamlit for the interface, NetworkX for graph modeling and analysis, and Plotly for interactive visualization, the project simulates live graph updates and presents key structural metrics including density, average degree, clustering coefficient, modularity, connectivity, and community distribution. Users can explore the network through different layout algorithms, filter by community, inspect high-centrality nodes, and track how the network changes over time through update history and trend charts.
Core Technologies
The dashboard is built with Python and Streamlit, using NetworkX for graph generation, analysis, centrality calculations, and community detection. Plotly powers the interactive node-link network visualization and time-series charts, while Pandas and NumPy support data handling and numerical operations. The project also includes configurable simulation settings for network type, update frequency, edge additions/removals, node sizing, and layout tuning, making it adaptable for experimentation and educational network analysis scenarios.
Key Features
🗹Interactive real-time network visualization with zoom, pan, hover details, and node labels
🗹Support for multiple network models including Barabasi-Albert, Erdos-Renyi, and Watts-Strogatz
🗹Live calculation of graph metrics such as density, average degree, clustering coefficient, modularity, and diameter
🗹Community detection and color-coded node grouping for easier structural analysis
🗹Centrality analysis with filtering for top influential nodes and community-based subgraph exploration
🗹Auto-refresh and manual refresh controls to simulate and inspect evolving network states
🗹Update history and network evolution tracking to monitor how nodes, edges, and density change over time
🗹Configurable visualization layouts including spring, circular, and Kamada-Kawai for different analysis perspectives
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