Summary of Graph Network Models to Detect Illicit Transactions in Block Chain, by Hrushyang Adloori et al.
Graph Network Models To Detect Illicit Transactions In Block Chain
by Hrushyang Adloori, Vaishnavi Dasanapu, Abhijith Chandra Mergu
First submitted to arxiv on: 23 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed approach utilizes graph attention networks with residual network-like architecture (GAT-ResNet) to detect illicit transactions related to anti-money laundering/combating the financing of terrorism (AML/CFT) in blockchains. This novel method is trained on the Elliptic Bitcoin Transaction dataset, comparing models such as logistic regression, Random Forest, XGBoost, GCN, GAT, and GAT-ResNet. The results show that GAT-ResNet has the potential to outperform existing graph network models in terms of accuracy, reliability, and scalability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way is being explored to stop bad money deals on the internet. Right now, people are using special computer networks called blockchains to hide illegal transactions. But a team of researchers thinks they can find these bad deals by using special math tools. They tested different approaches on a big dataset and found that one method, called GAT-ResNet, is really good at spotting the bad deals. This could help make it harder for bad people to use blockchains to hide their illegal activities. |
Keywords
» Artificial intelligence » Attention » Gcn » Logistic regression » Random forest » Residual network » Resnet » Xgboost
