The Shape of Money Laundering: Subgraph Representation Learning on the Blockchain with the Elliptic2 Dataset

• URL: http://arxiv.org/abs/2404.19109v3
• Date: Mon, 29 Jul 2024 10:53:34 GMT
• Title: The Shape of Money Laundering: Subgraph Representation Learning on the Blockchain with the Elliptic2 Dataset
• Authors: Claudio Bellei, Muhua Xu, Ross Phillips, Tom Robinson, Mark Weber, Tim Kaler, Charles E. Leiserson, Arvind, Jie Chen,
• Abstract summary: Subgraph representation learning is a technique for analyzing local structures (or shapes) within complex networks. We introduce Elliptic2, a large graph dataset containing 122K labeled subgraphs of Bitcoin clusters. We find immediate practical value in this approach and the potential for a new standard in anti-money laundering and forensic analytics in cryptocurrencies.
• Score: 6.209290101460395
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Subgraph representation learning is a technique for analyzing local structures (or shapes) within complex networks. Enabled by recent developments in scalable Graph Neural Networks (GNNs), this approach encodes relational information at a subgroup level (multiple connected nodes) rather than at a node level of abstraction. We posit that certain domain applications, such as anti-money laundering (AML), are inherently subgraph problems and mainstream graph techniques have been operating at a suboptimal level of abstraction. This is due in part to the scarcity of annotated datasets of real-world size and complexity, as well as the lack of software tools for managing subgraph GNN workflows at scale. To enable work in fundamental algorithms as well as domain applications in AML and beyond, we introduce Elliptic2, a large graph dataset containing 122K labeled subgraphs of Bitcoin clusters within a background graph consisting of 49M node clusters and 196M edge transactions. The dataset provides subgraphs known to be linked to illicit activity for learning the set of "shapes" that money laundering exhibits in cryptocurrency and accurately classifying new criminal activity. Along with the dataset we share our graph techniques, software tooling, promising early experimental results, and new domain insights already gleaned from this approach. Taken together, we find immediate practical value in this approach and the potential for a new standard in anti-money laundering and forensic analytics in cryptocurrencies and other financial networks.

Related papers

• Identifying Money Laundering Subgraphs on the Blockchain [5.377744640870357]
  Anti-Money Laundering involves the identification of money laundering crimes in financial activities. Recent studies advanced AML through the lens of graph-based machine learning. RevTrack is a graph-based framework that enables large-scale AML analysis with a lower cost and a higher accuracy.
  arXiv  Detail & Related papers  (2024-10-10T22:01:14Z)
• Redundancy-Free Self-Supervised Relational Learning for Graph Clustering [13.176413653235311]
  We propose a novel self-supervised deep graph clustering method named Redundancy-Free Graph Clustering (R$2$FGC) It extracts the attribute- and structure-level relational information from both global and local views based on an autoencoder and a graph autoencoder. Our experiments are performed on widely used benchmark datasets to validate the superiority of our R$2$FGC over state-of-the-art baselines.
  arXiv  Detail & Related papers  (2023-09-09T06:18:50Z)
• BOURNE: Bootstrapped Self-supervised Learning Framework for Unified Graph Anomaly Detection [50.26074811655596]
  We propose a novel unified graph anomaly detection framework based on bootstrapped self-supervised learning (named BOURNE) By swapping the context embeddings between nodes and edges, we enable the mutual detection of node and edge anomalies. BOURNE can eliminate the need for negative sampling, thereby enhancing its efficiency in handling large graphs.
  arXiv  Detail & Related papers  (2023-07-28T00:44:57Z)
• Finding Money Launderers Using Heterogeneous Graph Neural Networks [0.0]
  This paper introduces a graph neural network (GNN) approach to identify money laundering activities within a large heterogeneous network. We extend the homogeneous GNN method known as the Message Passing Neural Network (MPNN) to operate effectively on a heterogeneous graph. Our findings highlight the importance of using an appropriate GNN architecture when combining information in heterogeneous graphs.
  arXiv  Detail & Related papers  (2023-07-25T13:49:15Z)
• Detecting Anomalous Cryptocurrency Transactions: an AML/CFT Application of Machine Learning-based Forensics [5.617291981476445]
  The paper analyzes a real-world dataset of Bitcoin transactions represented as a directed graph network through various techniques. It shows that the neural network types known as Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) are a promising AML/CFT solution.
  arXiv  Detail & Related papers  (2022-06-07T16:22:55Z)
• Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction [123.20238648121445]
  We propose a new self-supervised learning framework, Graph Information Aided Node feature exTraction (GIANT) GIANT makes use of the eXtreme Multi-label Classification (XMC) formalism, which is crucial for fine-tuning the language model based on graph information. We demonstrate the superior performance of GIANT over the standard GNN pipeline on Open Graph Benchmark datasets.
  arXiv  Detail & Related papers  (2021-10-29T19:55:12Z)
• GraphMI: Extracting Private Graph Data from Graph Neural Networks [59.05178231559796]
  We present textbfGraph textbfModel textbfInversion attack (GraphMI), which aims to extract private graph data of the training graph by inverting GNN. Specifically, we propose a projected gradient module to tackle the discreteness of graph edges while preserving the sparsity and smoothness of graph features. We design a graph auto-encoder module to efficiently exploit graph topology, node attributes, and target model parameters for edge inference.
  arXiv  Detail & Related papers  (2021-06-05T07:07:52Z)
• Multi-Graph Tensor Networks [23.030263841031633]
  We introduce a novel Multi-Graph Network (MGTN) framework, which exploits the ability of graphs to handle irregular data sources and the compression properties of tensor networks in a deep learning setting. By virtue of the MGTN, a FOREX currency graph is leveraged to impose an economically meaningful structure on this demanding task, resulting in a highly superior performance against three competing models and at a drastically lower complexity.
  arXiv  Detail & Related papers  (2020-10-25T20:14:57Z)
• Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning [64.98816284854067]
  Graph-based Semi-Supervised Learning (SSL) aims to transfer the labels of a handful of labeled data to the remaining massive unlabeled data via a graph. A novel GCN-based SSL algorithm is presented in this paper to enrich the supervision signals by utilizing both data similarities and graph structure.
  arXiv  Detail & Related papers  (2020-09-15T13:59:28Z)
• Graph Prototypical Networks for Few-shot Learning on Attributed Networks [72.31180045017835]
  We propose a graph meta-learning framework -- Graph Prototypical Networks (GPN) GPN is able to perform textitmeta-learning on an attributed network and derive a highly generalizable model for handling the target classification task.
  arXiv  Detail & Related papers  (2020-06-23T04:13:23Z)
• Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs [54.13919050090926]
  We propose an end-to-end structural temporal Graph Neural Network model for detecting anomalous edges in dynamic graphs. In particular, we first extract the $h$-hop enclosing subgraph centered on the target edge and propose the node labeling function to identify the role of each node in the subgraph. Based on the extracted features, we utilize Gated recurrent units (GRUs) to capture the temporal information for anomaly detection.
  arXiv  Detail & Related papers  (2020-05-15T09:17:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.