Network Graph Analysis (Social Network Analysis)

[muskanhossain]

This technique maps users as nodes and calls as edges, revealing:

Communication clusters

Influencers or central figures

Hidden connections between groups
Used heavily in criminal investigations and marketing.

g. Anomaly Detection
AI or statistical models detect greece phone number data from normal behavior:

Unusual spikes in call volume or duration

Calls to new or blocked locations

Sudden disappearance from call networks

h. Clustering and Segmentation
Machine learning groups users by call behavior:

Business users vs. casual users

High-value vs. low-engagement users

Tourists vs. locals (based on roaming data)

i. Predictive Modeling
Historical call data can be used to forecast:

Future calling behavior

Churn likelihood

Potential fraud or threats

3. Data Sources for Call Pattern Analysis
Effective CPA depends on diverse and reliable data inputs. These include:

a. Call Detail Records (CDRs)
Primary source capturing all call metadata. Telecom providers store CDRs for billing, analysis, and legal purposes.

b. Short Message Service Records
Used to analyze text messaging behavior, often combined with call data.

c. VoIP and App-based Calls
Platforms like WhatsApp, Skype, and Zoom also log call metadata, useful for digital communication analysis.

d. Cell Tower Logs
Essential for geospatial analysis; reveal where the user was during a call.

e. Customer Relationship Management (CRM) Systems
Track call logs between companies and customers for sales or support purposes.

f. Mobile Device Logs
On-device analytics for app-based call data, subject to user permissions.