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Competence of graph convolutional network in anti-money laundering in Bitcoin Blockchain.

Alarab, I., Prakoonwit, S. and Nacer, M.I., 2020. Competence of graph convolutional network in anti-money laundering in Bitcoin Blockchain. In: ICMLT 2020: 5th International Conference on Machine Learning Technologies, 19 - 21 June 2020, Bejing, China.

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BRIANCompetenceOfGraphConvolutionalNetworksForAntiMoneyLaunderingInBitcoinBlockchain.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial.


DOI: 10.1145/3409073.3409080


Graph networks are extensively used as an essential framework to analyse the interconnections between transactions and capture illicit behaviour in Bitcoin blockchain. Due to the complexity of Bitcoin transaction graph, the prediction of illicit transactions has become a challenging problem to unveil illicit services over the network. Graph Convolutional Network, a graph neural network based spectral approach, has recently emerged and gained much attention regarding graph-structured data. Previous research has highlighted the degraded performance of the latter approach to predict illicit transactions using, a Bitcoin transaction graph, so-called Elliptic data derived from Bitcoin blockchain. Motivated by the previous work, we seek to explore graph convolutions in a novel way. For this purpose, we present a novel approach that is modelled using the existing Graph Convolutional Network intertwined with linear layers. Concisely, we concatenate node embeddings obtained from graph convolutional layers with a single hidden layer derived from the linear transformation of the node feature matrix and followed by Multi-layer Perceptron. Our approach is evaluated using Elliptic data, wherein efficient accuracy is yielded. The proposed approach outperforms the original work of same data set.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:Graph Convolutional Network; Supervised Learning; Anomaly Detection; Anti-Money Laundering; Bitcoin Blockchain
Group:Faculty of Science & Technology
ID Code:34650
Deposited By: Symplectic RT2
Deposited On:02 Oct 2020 15:54
Last Modified:14 Mar 2022 14:24


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