Graph Regularized Nonnegative Latent Factor Analysis Model for Temporal Link Prediction in Cryptocurrency Transaction Networks

• URL: http://arxiv.org/abs/2208.01923v1
• Date: Wed, 3 Aug 2022 08:58:59 GMT
• Title: Graph Regularized Nonnegative Latent Factor Analysis Model for Temporal Link Prediction in Cryptocurrency Transaction Networks
• Authors: Zhou Yue, Liu ZhiGang, Yuan Ye
• Abstract summary: Link prediction learning structure of network is helpful to understand the mechanism of network. We propose a single latent factor-dependent, non-negative, multiplicative and graph regularized-incorporated update (SLF-NMGRU) algorithm. Experiments on a real cryptocurrency transaction network show that the proposed method improves both the accuracy and the computational efficiency.
• Score: 1.6801544027052142
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: With the development of blockchain technology, the cryptocurrency based on blockchain technology is becoming more and more popular. This gave birth to a huge cryptocurrency transaction network has received widespread attention. Link prediction learning structure of network is helpful to understand the mechanism of network, so it is also widely studied in cryptocurrency network. However, the dynamics of cryptocurrency transaction networks have been neglected in the past researches. We use graph regularized method to link past transaction records with future transactions. Based on this, we propose a single latent factor-dependent, non-negative, multiplicative and graph regularized-incorporated update (SLF-NMGRU) algorithm and further propose graph regularized nonnegative latent factor analysis (GrNLFA) model. Finally, experiments on a real cryptocurrency transaction network show that the proposed method improves both the accuracy and the computational efficiency

Related papers

• The Latency Price of Threshold Cryptosystem in Blockchains [52.359230560289745]
  We study the interplay between threshold cryptography and a class of blockchains that use Byzantine-fault tolerant (BFT) consensus protocols. Existing approaches for threshold cryptosystems introduce a latency overhead of at least one message delay for running the threshold cryptographic protocol. We propose a mechanism to eliminate this overhead for blockchain-native threshold cryptosystems with tight thresholds.
  arXiv  Detail & Related papers  (2024-07-16T20:53:04Z)
• The Kosmosis Use-Case of Crypto Rug Pull Detection and Prevention [0.0]
  Current methods to prevent crypto asset fraud are based on the analysis of transaction graphs within blockchain networks. We propose the Kosmosis approach, which aims to incrementally construct a knowledge graph as new blockchain and social media data become available.
  arXiv  Detail & Related papers  (2024-05-30T07:17:57Z)
• Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning [51.13534069758711]
  Decentralized approaches like blockchain offer a compelling solution by implementing a consensus mechanism among multiple entities. Federated Learning (FL) enables participants to collaboratively train models while safeguarding data privacy. This paper investigates the synergy between blockchain's security features and FL's privacy-preserving model training capabilities.
  arXiv  Detail & Related papers  (2024-03-28T07:08:26Z)
• Graph Attention Network-based Block Propagation with Optimal AoI and Reputation in Web 3.0 [59.94605620983965]
  We design a Graph Attention Network (GAT)-based reliable block propagation optimization framework for blockchain-enabled Web 3.0. To achieve the reliability of block propagation, we introduce a reputation mechanism based on the subjective logic model. Considering that the GAT possesses the excellent ability to process graph-structured data, we utilize the GAT with reinforcement learning to obtain the optimal block propagation trajectory.
  arXiv  Detail & Related papers  (2024-03-20T01:58:38Z)
• Blockchain Metrics and Indicators in Cryptocurrency Trading [0.22940141855172028]
  The objective of this paper is the construction of new indicators that can be useful to operate in the cryptocurrency market. These indicators are based on public data obtained from the blockchain network, specifically from the nodes that make up Bitcoin mining.
  arXiv  Detail & Related papers  (2024-02-11T12:34:58Z)
• Generative AI-enabled Blockchain Networks: Fundamentals, Applications, and Case Study [73.87110604150315]
  Generative Artificial Intelligence (GAI) has emerged as a promising solution to address challenges of blockchain technology. In this paper, we first introduce GAI techniques, outline their applications, and discuss existing solutions for integrating GAI into blockchains.
  arXiv  Detail & Related papers  (2024-01-28T10:46:17Z)
• 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)
• Motif-aware temporal GCN for fraud detection in signed cryptocurrency trust networks [8.82136716762572]
  Graph convolutional networks (GCNs) are used for processing data that can be represented as graphs. In this study, we consider the evolving nature of cryptocurrency networks, and use local structural as well as the balance theory to guide the training process. Experimental results on bitcoin-alpha and bitcoin-otc datasets show that the proposed model outperforms those in the literature.
  arXiv  Detail & Related papers  (2022-11-22T02:03:27Z)
• 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)
• TSGN: Transaction Subgraph Networks for Identifying Ethereum Phishing Accounts [2.3112192919085826]
  Transaction SubGraph Network (TSGN) based classification model to identify phishing accounts. We find that TSGNs can provide more potential information to benefit the identification of phishing accounts.
  arXiv  Detail & Related papers  (2021-04-18T08:12:51Z)
• Wide and Deep Graph Neural Networks with Distributed Online Learning [175.96910854433574]
  Graph neural networks (GNNs) learn representations from network data with naturally distributed architectures. Online learning can be used to retrain GNNs at testing time, overcoming this issue. This paper proposes the Wide and Deep GNN (WD-GNN), a novel architecture that can be easily updated with distributed online learning mechanisms.
  arXiv  Detail & Related papers  (2020-06-11T12:48:03Z)

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.