Criminal Network Analysis and Visualization

Overview

A visualization tool for criminal network analysis.

• analyzer: criminal (social) network analysis functionalities, including community detection, social influence analysis, node embedding generation, and (transductive) link prediction.
• datasets: criminal datasets for developing/testing the system. [List of Criminal Datasets].
• conductor: providing API services
• framework: the system's infrastructure/framework and communication among its components
• storage: data handling graph storage
• tester: example scripts for testing the system's components
• visualizer: web interfaces and visualization. [Visualizer User Documentation].

This repository is a part of the paper "Inductive and Transductive Link Prediction for Criminal Network Analysis," published in the Journal of Computational Science in 2023. The paper discusses how identifying potential offenders who might co-offend can help law enforcement focus their investigations and improve predictive policing. Traditional methods rely heavily on manual work by police officers, which can be inefficient. To address this, the paper introduces two machine learning frameworks based on graph theory, specifically for burglary cases. These are transductive link prediction (this repository), which predicts connections between existing nodes (offenders or crime cases), and inductive link prediction (see the implementation here), which finds links between new crime cases and existing nodes.

Citation

Ahmadi, Z., Nguyen, H. H., Zhang, Z., Bozhkov, D., Kudenko, D., Jofre, M., Calderoni, F., Cohen, N., & Solewicz, Y. (2023). Inductive and transductive link prediction for criminal network analysis. Journal of Computational Science. Preprint

@article{ahmadi2023inductive,
  title = {Inductive and Transductive Link Prediction for Criminal Network Analysis},
  author = {Ahmadi, Zahra and Nguyen, Hoang H. and Zhang, Zijian and Bozhkov, Dmytro and Kudenko, Daniel and Jofre, Maria and Calderoni, Francesco and Cohen, Noa and Solewicz, Yosef},
  journal = {Journal of Computational Science},
  publisher = {Elsevier},
  volume = {72},
  pages = {102063},
  year = {2023},
  issn = {1877-7503},
  doi = {https://doi.org/10.1016/j.jocs.2023.102063},
  url = {https://www.sciencedirect.com/science/article/pii/S1877750323001230},
}

Analysis and Visualization

Installation

Requirements

• Linux
• Anaconda

Install Environment

Install python required packages.

pip install -r visualizer/requirements.txt

Quickstart

Run the server

Start the server by executing the index.py with Python 3.7:

$ python visualizer/index.py

Open the visualizer

The server now listens locally on port 8050. Click the link provided by the command line interface or visit 0.0.0.0:8050 directly.

Visualizer User Documentation

For a full tutorial on how to use our visualizer, please check the Visualizer User Documentation.