Decoding Decentralized Finance Transactions through Ego Network Motif Mining

• URL: http://arxiv.org/abs/2408.12311v1
• Date: Thu, 22 Aug 2024 11:38:59 GMT
• Title: Decoding Decentralized Finance Transactions through Ego Network Motif Mining
• Authors: Natkamon Tovanich, Célestin Coquidé, Rémy Cazabet,
• Abstract summary: This paper presents a method to extract ego network motifs from the token transfer network, capturing the transfer of tokens between users and smart contracts. Our results demonstrate that smart contract methods performing specific DeFi operations can be efficiently identified by analyzing these motifs.
• Score: 1.9253333342733674
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Decentralized Finance (DeFi) is increasingly studied and adopted for its potential to provide accessible and transparent financial services. Analyzing how investors use DeFi is important for reaching a better understanding of their usage and for regulation purposes. However, analyzing DeFi transactions is challenging due to often incomplete or inaccurate labeled data. This paper presents a method to extract ego network motifs from the token transfer network, capturing the transfer of tokens between users and smart contracts. Our results demonstrate that smart contract methods performing specific DeFi operations can be efficiently identified by analyzing these motifs while providing insights into account activities.

Related papers

• Maximal Extractable Value in Decentralized Finance: Taxonomy, Detection, and Mitigation [0.534772724436823]
  Maximal Extractable Value (MEV) can be extracted from financial transactions on the blockchain. MEV causes financial losses and consensus instability, disrupting the security, efficiency, and decentralization goals of the DeFi ecosystem. This survey provides valuable insights for researchers, developers, stakeholders, and policymakers.
  arXiv  Detail & Related papers  (2024-10-22T12:11:41Z)
• Advancing Anomaly Detection: Non-Semantic Financial Data Encoding with LLMs [49.57641083688934]
  We introduce a novel approach to anomaly detection in financial data using Large Language Models (LLMs) embeddings. Our experiments demonstrate that LLMs contribute valuable information to anomaly detection as our models outperform the baselines.
  arXiv  Detail & Related papers  (2024-06-05T20:19:09Z)
• Cryptocurrency Portfolio Optimization by Neural Networks [81.20955733184398]
  This paper proposes an effective algorithm based on neural networks to take advantage of these investment products. A deep neural network, which outputs the allocation weight of each asset at a time interval, is trained to maximize the Sharpe ratio. A novel loss term is proposed to regulate the network's bias towards a specific asset, thus enforcing the network to learn an allocation strategy that is close to a minimum variance strategy.
  arXiv  Detail & Related papers  (2023-10-02T12:33:28Z)
• Transaction Fraud Detection via an Adaptive Graph Neural Network [64.9428588496749]
  We propose an Adaptive Sampling and Aggregation-based Graph Neural Network (ASA-GNN) that learns discriminative representations to improve the performance of transaction fraud detection. A neighbor sampling strategy is performed to filter noisy nodes and supplement information for fraudulent nodes. Experiments on three real financial datasets demonstrate that the proposed method ASA-GNN outperforms state-of-the-art ones.
  arXiv  Detail & Related papers  (2023-07-11T07:48:39Z)
• Scalable and Weakly Supervised Bank Transaction Classification [0.0]
  This paper aims to categorize bank transactions using weak supervision, natural language processing, and deep neural network training. We present an effective and scalable end-to-end data pipeline, including data preprocessing, transaction text embedding, anchoring, label generation, discriminative neural network training.
  arXiv  Detail & Related papers  (2023-05-28T23:12:12Z)
• Leveraging Machine Learning for Multichain DeFi Fraud Detection [5.213509776274283]
  We present a framework for extracting features from different chains, including the largest one, and it is evaluated over an extensive dataset. Different Machine Learning methods were employed, such as XGBoost and a Neural Network for identifying fraud accounts detection interacting with DeFi. We demonstrate that the introduction of novel DeFi-related features, significantly improves the evaluation results.
  arXiv  Detail & Related papers  (2023-05-17T15:48:21Z)
• Semantic Information Marketing in The Metaverse: A Learning-Based Contract Theory Framework [68.8725783112254]
  We address the problem of designing incentive mechanisms by a virtual service provider (VSP) to hire sensing IoT devices to sell their sensing data. Due to the limited bandwidth, we propose to use semantic extraction algorithms to reduce the delivered data by the sensing IoT devices. We propose a novel iterative contract design and use a new variant of multi-agent reinforcement learning (MARL) to solve the modelled multi-dimensional contract problem.
  arXiv  Detail & Related papers  (2023-02-22T15:52:37Z)
• 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)
• VeriFi: Towards Verifiable Federated Unlearning [59.169431326438676]
  Federated learning (FL) is a collaborative learning paradigm where participants jointly train a powerful model without sharing their private data. Leaving participant has the right to request to delete its private data from the global model. We propose VeriFi, a unified framework integrating federated unlearning and verification.
  arXiv  Detail & Related papers  (2022-05-25T12:20:02Z)
• Detecting DeFi Securities Violations from Token Smart Contract Code [0.4263043028086136]
  Decentralized Finance (DeFi) is a system of financial products and services built and delivered through smart contracts on various blockchains. This study aims to uncover whether we can identify DeFi projects potentially engaging in securities violations based on their tokens' smart contract code.
  arXiv  Detail & Related papers  (2021-12-06T01:44:08Z)
• Relational Graph Neural Networks for Fraud Detection in a Super-App environment [53.561797148529664]
  We propose a framework of relational graph convolutional networks methods for fraudulent behaviour prevention in the financial services of a Super-App. We use an interpretability algorithm for graph neural networks to determine the most important relations to the classification task of the users. Our results show that there is an added value when considering models that take advantage of the alternative data of the Super-App and the interactions found in their high connectivity.
  arXiv  Detail & Related papers  (2021-07-29T00:02:06Z)

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.