analyze-communications

To analyze communication networks, this Skill processes emails, texts, calls, and chat logs to generate interactive network graphs, centrality rankings, and community detections. You simply provide the data path and an output directory to build relationship mappings from your files.

Identifying key players in phone records and text logs is done by calculating centrality metrics within the communication network. This Skill applies network analysis to rank the most active participants and detect distinct communities across your multi-channel data.

Yes, you can visualize temporal gaps in WhatsApp exports using Python, NetworkX, and Plotly. This Skill applies temporal analysis to your chat logs to surface timeline anomalies and outputs interactive HTML visualizations of the communication patterns.

Yes, this communication network analysis tool supports common data formats including CSV and Excel files. It uses pandas and openpyxl to read your communication data, allowing you to process single files or entire directories of records.

You generate interactive HTML visualizations from communication data by running the analysis script with your directory path. The Skill uses Plotly to produce network graphs, heatmaps, and timelines, alongside structured reports for case strategy.

While effective for relationship mapping, limitations include requiring Python environments with specific dependencies like pandas, NetworkX, and Plotly. It processes common formats like CSV and Excel, but extremely large datasets may impact performance during temporal analysis.