Characterizing Polkadot's Transactions Ecosystem: methodology, tools, and insights

• URL: http://arxiv.org/abs/2404.10543v1
• Date: Tue, 16 Apr 2024 13:11:02 GMT
• Title: Characterizing Polkadot's Transactions Ecosystem: methodology, tools, and insights
• Authors: Maurantonio Caprolu, Roberto Di Pietro, Flavio Lombardi, Elia Onofri,
• Abstract summary: Polkadot has gained significant attention in the digital currency landscape due to its pioneering approach to interoperability and scalability. We map Polkadot on a palette that ranges from a thriving ecosystem to a speculative coin without compelling use cases. Our findings demonstrate that crypto exchanges exert considerable influence on the Polkadot network, owning nearly 40% of all addresses in the ledger and absorbing at least 80% of all transactions.
• Score: 1.912429179274357
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The growth potential of a crypto(currency) project can be measured by the use cases spurred by the underlying technology. However, these projects are usually distributed, with a weak feedback schemes. Hence, a metric that is widely used as a proxy for their healthiness is the number of transactions and related volumes. Nevertheless, such a metric can be subject to manipulation (the crypto market being an unregulated one magnifies such a risk). To address the cited gap we design a comprehensive methodology to process large cryptocurrency transaction graphs that, after clustering user addresses of interest, derives a compact representation of the network that highlights clusters interactions. To show the viability of our solution, we bring forward a use case centered on Polkadot, which has gained significant attention in the digital currency landscape due to its pioneering approach to interoperability and scalability. However, little is known about how many and to what extent its wide range of enabled use cases have been adopted by end-users so far. The answer to this type of question means mapping Polkadot (or any analyzed crypto project) on a palette that ranges from a thriving ecosystem to a speculative coin without compelling use cases. Our findings demonstrate that crypto exchanges exert considerable influence on the Polkadot network, owning nearly 40% of all addresses in the ledger and absorbing at least 80% of all transactions. In addition, the high volume of inter-exchange transactions (> 20%) underscores the strong interconnections among just a couple of prominent exchanges, prompting further investigations into the behavior of these actors to uncover potential unethical activities, such as wash trading. These results, while characterized by a high level of scalability and adaptability, are at the same time immune from the drawbacks of currently used metrics.

Related papers

• 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)
• Topology-Agnostic Detection of Temporal Money Laundering Flows in Billion-Scale Transactions [0.03626013617212666]
  We propose a framework to efficiently construct a temporal graph of sequential transactions. We evaluate the scalability and the effectiveness of our framework against two state-of-the-art solutions for detecting suspicious flows of transactions.
  arXiv  Detail & Related papers  (2023-09-24T15:11:58Z)
• 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)
• Blockchain Large Language Models [65.7726590159576]
  This paper presents a dynamic, real-time approach to detecting anomalous blockchain transactions. The proposed tool, BlockGPT, generates tracing representations of blockchain activity and trains from scratch a large language model to act as a real-time Intrusion Detection System.
  arXiv  Detail & Related papers  (2023-04-25T11:56:18Z)
• Towards Measuring the Traceability of Cryptocurrencies [0.5371337604556311]
  We put forward a formal framework to measure the (un)traceability and anonymity of cryptocurrencies. Our work provides the first practical, efficient, and probabilistic measure to assess the traceability of cryptocurrencies. We implement and extensively evaluate our proposed traceability measure on several cryptocurrency transaction graphs.
  arXiv  Detail & Related papers  (2022-11-08T14:08:39Z)
• 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)
• 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)
• Coarse to Fine: Domain Adaptive Crowd Counting via Adversarial Scoring Network [58.05473757538834]
  This paper proposes a novel adversarial scoring network (ASNet) to bridge the gap across domains from coarse to fine granularity. Three sets of migration experiments show that the proposed methods achieve state-of-the-art counting performance.
  arXiv  Detail & Related papers  (2021-07-27T14:47:24Z)
• Identity Inference on Blockchain using Graph Neural Network [5.5927440285709835]
  Identity inference, which aims to make a preliminary inference about account identity, plays a significant role in blockchain security. We present a novel approach to analyze user's behavior from the perspective of the transaction subgraph, which naturally transforms the identity inference task into a graph classification pattern. We also propose a generic end-to-end graph neural network model, named $textI2 textBGNN$, which can accept subgraph as input and learn a function mapping the transaction subgraph pattern to account identity.
  arXiv  Detail & Related papers  (2021-04-14T00:15:38Z)
• Multi-scale Interactive Network for Salient Object Detection [91.43066633305662]
  We propose the aggregate interaction modules to integrate the features from adjacent levels. To obtain more efficient multi-scale features, the self-interaction modules are embedded in each decoder unit. Experimental results on five benchmark datasets demonstrate that the proposed method without any post-processing performs favorably against 23 state-of-the-art approaches.
  arXiv  Detail & Related papers  (2020-07-17T15:41:37Z)
• Characterizing and Detecting Money Laundering Activities on the Bitcoin Network [8.212945859699406]
  We explore the landscape of potential money laundering activities occurring across the Bitcoin network. Using data collected over three years, we create transaction graphs and provide an analysis on various graph characteristics to differentiate money laundering transactions from regular transactions. We propose and evaluate a set of classifiers based on four types of graph features to classify money laundering and regular transactions.
  arXiv  Detail & Related papers  (2019-12-27T11:34:41Z)

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.