Unlock Social Insights with Osintgraph: Mapping Instagram Networks Using Neo4j

The Power of Social Network Analysis

In today’s interconnected world, social relationships reveal more about individuals than surface-level profiles suggest. Osintgraph bridges the gap between Instagram’s social data and professional network analysis through Neo4j’s graph database technology. This powerful combination transforms social connections into actionable intelligence for legitimate research purposes.

Core Functionality Explained

🔧 Essential Command Toolkit

Command Function Usage Example
-setup Connects Neo4j and logs into Instagram python main.py -setup
-discover Retrieves user metadata and relationships -discover "username" -follower_limit 2000
-explore Automatically maps target’s network -explore "username" -max_people 10
-resume_fetching Continues incomplete data collection -resume_fetching -max_rounds 5

⚙️ Global Configuration Options

# Adjust default limits (1000 each)
-follower_limit 1500  
-followee_limit 1500

# Enable troubleshooting
-debug

# Prevent detection
-rate_limit 200  # Pauses after 200 requests

Practical Insights from Network Analysis

📍 Geographical Connections

When targets and their connections follow local businesses or community pages, we can:

  1. Identify frequent location tags
  2. Map clustered business connections
  3. Cross-reference check-in patterns

https://github.com/user-attachments/assets/71a7bd04-7a5f-4e59-9bb2-2233ce62423b

👥 Professional & Educational Context

Network patterns reveal:

  • Corporate account connections → Career indicators
  • Alumni groups → Educational background
  • Industry communities → Professional interests

🎭 Interest Profiling

graph LR
A[Target Account] --> B(Sports Clubs)
A --> C(Art Galleries)
A --> D(Tech Brands)
B & C & D --> E[Interest Profile]

🌐 Cultural & Linguistic Clues

Observing:

  • Regional language accounts
  • Local festival participation
  • Geographic content preferences

👨👩👧👦 Relationship Mapping

Strong connection indicators:

  • Mutual follows
  • High interaction frequency
  • Shared communities
  • Profile similarities

🕵️ Multiple Account Detection

Spotting alternate accounts through:

  • Overlapping follower circles
  • Similar posting schedules
  • Matching behavioral patterns
  • Connected private/public profiles

📊 Behavioral Profiling

Follow Patterns Likely Characteristics
Business-focused Career-oriented
Travel influencers Frequent traveler
Local news sources Community-engaged

Step-by-Step Implementation Guide

🖥 Environment Setup

  1. Create Neo4j Instance

    • Visit https://neo4j.com
    • Select “Get Started Free”
    • Save administrator credentials

  2. Install Osintgraph

    git clone https://github.com/XD-MHLOO/Osintgraph.git
cd Osintgraph
pip install -r requirements.txt

🔑 Initial Configuration

python main.py -setup

When prompted:

  1. Enter Neo4j connection details
  2. Provide Instagram credentials
  3. Supply browser User-Agent string

📊 Data Collection Methods

Basic discovery:

python main.py -discover "target_username"

Network exploration:

# Analyze 10 connections
python main.py -explore "username" -max_people 10 

# Resume partial collections
python main.py -resume_fetching -max_rounds 3

🌐 Data Visualization

  1. Access https://console-preview.neo4j.io/tools/explore
  2. Enter query: SHOW ME A GRAPH
  3. Explore connection patterns

https://github.com/user-attachments/assets/71a7bd04-7a5f-4e59-9bb2-2233ce62423b

🛡️ Account Security Protocol

  1. Login Methodology

    + Use Firefox session authentication
- Avoid direct credential input

  2. Device Identification

    # Provide actual browser signature
-user_agent "Mozilla/5.0 (Windows NT 10.0; Win64; x64)"

  3. Risk Mitigation

    • ✅ Activate two-factor authentication
    • ✅ Establish account history before scraping
    • ❌ Disable VPN connections
    • ⏱ Limit sessions to <6 hours

  4. Operation Isolation

    During scraping:
└─ No browsing activity
└─ No content interaction
└─ No device switching

Technical Implementation

📦 Core Components

Component Function Version
Instaloader Data collection engine ≥4.9.1
Neo4j Graph database AuraDB Free

⚙️ Data Model Structure

(:USER {
  username: "example",
  posts_count: 150,
  is_private: false
})-[:FOLLOWS]->(:USER)

Frequently Asked Questions

❓ Can I analyze private accounts?

“

Yes. By examining public connections (friends, communities, interactions), you can reconstruct network attributes without accessing private profiles directly.

❓ How to troubleshoot collection issues?

  1. Verify -rate_limit settings
  2. Enable -debug mode
  3. Execute -resume_fetching in stages

❓ What’s the most efficient discovery method?

python main.py -explore "central_user" \
    -max_people 20 \
    -follower_limit 1500 \
    -rate_limit 300

❓ How to track changes over time?

Schedule periodic:

python main.py -resume_fetching -max_rounds 5

Compare results to identify:

  • New connections
  • Relationship changes
  • Interest evolution

Ethical Implementation Framework

Osintgraph reveals powerful social insights that require responsible application:

  1. Strictly comply with Instagram’s Terms of Service
  2. Respect privacy boundaries
  3. Employ only for legitimate research purposes
  4. Maintain transparency about data usage

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Security acknowledgments: https://github.com/ahmdrz
Project repository: https://github.com/XD-MHLOO/Osintgraph