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Detecting fraud rings in a p2p trust network with high AUC score (better than 9 of 10 well-known algorithms)* using basic and intuitive trust metrics

License: MIT License

Python 0.09% Jupyter Notebook 99.91%
data-science fraud fraud-detection graph graph-theory machine-learning network network-science social-network-analysis

fraudringdetection-trustnetworks-trying-new-approach's Introduction

๐Ÿ‘‹ Hi, I'm Umit

GitHub User's stars Views

Interests: Machine learning (time-series and graphs), Explainable AI, Social networks, Venture capital

Currently: Learning Lisp & CUDA

โญ My favorite projects

๐Ÿ•ต๏ธ reddit-detective: Detect political disinformation campaigns, discover how ideas spread between communities, find "cyborg-like" activities carried out by bots and more in Reddit Downloads

๐Ÿฐ Jomini: What if Byzantines had more soldiers in 1453? You can model this and many other historical battles Downloads

๐Ÿค TIA is an advanced Twitter stalking/analysis tool powered by machine learning.

๐Ÿ’ฐ Trying a New Fraud Detection Approach for Trust Networks While trying to detect fraud rings in the bitcoin-otc network, I came up with an individual fraud detection approach which is better than 9 of 10 well-known network-based fraud detection algorithms for this problem. (At least for this data set, I'll try it in different datasets and tune the models when I have time for that)

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fraudringdetection-trustnetworks-trying-new-approach's Issues

Migrate the project from SNAP to Neo4j and NetworkX

Reasons:

  • NetworkX implements a large number of network analysis methods
  • NetworkX offers great flexibility
  • Neo4j can be used in computationally heavier jobs where possible
  • Neo4j also makes visualization nearly effortless
  • The dataset is not high-volume (5.8k nodes, 35.6k edges), NetworkX's performance issues won't be a big deal

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