Data Depth and Core-based Trend Detection on Blockchain Transaction Networks

• URL: http://arxiv.org/abs/2303.14241v2
• Date: Sun, 5 May 2024 02:17:18 GMT
• Title: Data Depth and Core-based Trend Detection on Blockchain Transaction Networks
• Authors: Jason Zhu, Arijit Khan, Cuneyt Gurcan Akcora,
• Abstract summary: We introduce a method named InnerCore that detects market manipulators within blockchain-based networks. This is achieved through data depth-based core decomposition and centered motif discovery. We show that InnerCore can match the qualified analysis accurately without human involvement.
• Score: 9.07520594836878
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Blockchains are significantly easing trade finance, with billions of dollars worth of assets being transacted daily. However, analyzing these networks remains challenging due to the sheer volume and complexity of the data. We introduce a method named InnerCore that detects market manipulators within blockchain-based networks and offers a sentiment indicator for these networks. This is achieved through data depth-based core decomposition and centered motif discovery, ensuring scalability. InnerCore is a computationally efficient, unsupervised approach suitable for analyzing large temporal graphs. We demonstrate its effectiveness by analyzing and detecting three recent real-world incidents from our datasets: the catastrophic collapse of LunaTerra, the Proof-of-Stake switch of Ethereum, and the temporary peg loss of USDC - while also verifying our results against external ground truth. Our experiments show that InnerCore can match the qualified analysis accurately without human involvement, automating blockchain analysis in a scalable manner, while being more effective and efficient than baselines and state-of-the-art attributed change detection approach in dynamic graphs.

Related papers

• Optimizing Blockchain Analysis: Tackling Temporality and Scalability with an Incremental Approach with Metropolis-Hastings Random Walks [2.855856661274715]
  Existing methods primarily focus on snapshots of transaction networks. We propose an incremental approach for random walk-based node representation learning in transaction networks. Potential applications include transaction network monitoring, the efficient classification of blockchain addresses for fraud detection or the identification of specialized address types within the network.
  arXiv  Detail & Related papers  (2025-01-21T20:34:38Z)
• Blockchain Data Analysis in the Era of Large-Language Models [21.81035847078574]
  Existing blockchain data analysis tools face challenges, including data scarcity, the lack of generalizability, and the lack of reasoning capability. We believe large language models (LLMs) can mitigate these challenges. This paper systematically explores potential techniques and design patterns in LLM-integrated blockchain data analysis.
  arXiv  Detail & Related papers  (2024-12-09T07:32:35Z)
• Exact Certification of (Graph) Neural Networks Against Label Poisoning [50.87615167799367]
  Machine learning models are vulnerable to label flipping, i.e., the adversarial modification (poisoning) of training labels to compromise performance. We introduce an exact certification method, deriving both sample-wise and collective certificates. Our work presents the first exact certificate to a poisoning attack ever derived for neural networks, which could be of independent interest.
  arXiv  Detail & Related papers  (2024-11-30T17:05:12Z)
• Review of blockchain application with Graph Neural Networks, Graph Convolutional Networks and Convolutional Neural Networks [0.0]
  This paper reviews the applications of Graph Neural Networks (GNNs), Graph Convolutional Networks (GCNs), and Convolutional Neural Networks (CNNs) in blockchain technology.
  arXiv  Detail & Related papers  (2024-10-01T17:11:22Z)
• Provable Robustness of (Graph) Neural Networks Against Data Poisoning and Backdoor Attacks [50.87615167799367]
  We certify Graph Neural Networks (GNNs) against poisoning attacks, including backdoors, targeting the node features of a given graph. Our framework provides fundamental insights into the role of graph structure and its connectivity on the worst-case behavior of convolution-based and PageRank-based GNNs.
  arXiv  Detail & Related papers  (2024-07-15T16:12:51Z)
• Towards Practical Control of Singular Values of Convolutional Layers [65.25070864775793]
  Convolutional neural networks (CNNs) are easy to train, but their essential properties, such as generalization error and adversarial robustness, are hard to control. Recent research demonstrated that singular values of convolutional layers significantly affect such elusive properties. We offer a principled approach to alleviating constraints of the prior art at the expense of an insignificant reduction in layer expressivity.
  arXiv  Detail & Related papers  (2022-11-24T19:09:44Z)
• 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)
• Cryptocurrencies Activity as a Complex Network: Analysis of Transactions Graphs [7.58432869763351]
  We analyze the flow of these digital transactions in a certain period of time by studying, through complex network theory, the patterns of interactions in four prominent and different Distributed Ledger Technologies (DLTs) We show that studying the network characteristics and peculiarities is of paramount importance, in order to understand how users interact in the DLT.
  arXiv  Detail & Related papers  (2021-09-14T08:32:36Z)
• S2-BNN: Bridging the Gap Between Self-Supervised Real and 1-bit Neural Networks via Guided Distribution Calibration [74.5509794733707]
  We present a novel guided learning paradigm from real-valued to distill binary networks on the final prediction distribution. Our proposed method can boost the simple contrastive learning baseline by an absolute gain of 5.515% on BNNs. Our method achieves substantial improvement over the simple contrastive learning baseline, and is even comparable to many mainstream supervised BNN methods.
  arXiv  Detail & Related papers  (2021-02-17T18:59:28Z)
• Information Obfuscation of Graph Neural Networks [96.8421624921384]
  We study the problem of protecting sensitive attributes by information obfuscation when learning with graph structured data. We propose a framework to locally filter out pre-determined sensitive attributes via adversarial training with the total variation and the Wasserstein distance.
  arXiv  Detail & Related papers  (2020-09-28T17:55:04Z)
• Graph Backdoor [53.70971502299977]
  We present GTA, the first backdoor attack on graph neural networks (GNNs) GTA departs in significant ways: it defines triggers as specific subgraphs, including both topological structures and descriptive features. It can be instantiated for both transductive (e.g., node classification) and inductive (e.g., graph classification) tasks.
  arXiv  Detail & Related papers  (2020-06-21T19:45:30Z)

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.