相关论文: Heuristic-Based Address Clustering in Cardano Bloc…

We analyzed the associations between Bitcoin transactions and addresses to cluster address and further find groups of addresses controlled by the same entity. It revealed the vulnerabilities of Bitcoin anonymity mechanism, which could be…

Exploring transactions within the Bitcoin blockchain entails examining the transfer of bitcoins among several hundred million entities. However, it is often impractical and resource-consuming to study such a vast number of entities.…

The development of clustering heuristics has demonstrated that Bitcoin is not completely anonymous. Currently, existing clustering heuristics only consider confirmed transactions recorded in the Bitcoin blockchain. However, unconfirmed…

Blockchain analysis is essential for understanding how cryptocurrencies like Bitcoin are used in practice, and address clustering is a cornerstone of blockchain analysis. However, current techniques rely on heuristics that have not been…

One of the defining features of Bitcoin and the thousands of cryptocurrencies that have been derived from it is a globally visible transaction ledger. While Bitcoin uses pseudonyms as a way to hide the identity of its participants, a long…

Cluster analysis plays an important role in decision making process for many knowledge-based systems. There exist a wide variety of different approaches for clustering applications including the heuristic techniques, probabilistic models,…

Bitcoin is built on a blockchain, an immutable decentralised ledger that allows entities (users) to exchange Bitcoins in a pseudonymous manner. Bitcoins are associated with alpha-numeric addresses and are transferred via transactions. Each…

Address clustering tries to construct the one-to-many mapping from entities to addresses in the Bitcoin system. Simple heuristics based on the micro-structure of transactions have proved very effective in practice. In this paper we describe…

Anonymity is one of the most important qualities of blockchain technology. For example, one can simply create a bitcoin address to send and receive funds without providing KYC to any authority. In general, the real identity behind…

Clustering is an unsupervised machine learning task that consists of identifying groups of similar objects. It has numerous applications and is increasingly used in fairness-sensitive domains where objects represent individuals, such as…

This research delves into the intricacies of Bitcoin, a decentralized peer-to-peer network, and its associated blockchain, which records all transactions since its inception. While this ensures integrity and transparency, the transparent…

The distribution of consensus power is a cornerstone of decentralisation, influencing the security, resilience, and fairness of blockchain networks while ensuring equitable impact among participants. This study provides a rigorous…

Clustering a graph means identifying internally dense subgraphs which are only sparsely interconnected. Formalizations of this notion lead to measures that quantify the quality of a clustering and to algorithms that actually find…

Bitcoin (BTC) pseudonyms (layer 1) can effectively be deanonymized using heuristic clustering techniques. However, while performing transactions off-chain (layer 2) in the Lightning Network (LN) seems to enhance privacy, a systematic…

Clustering analysis is of substantial significance for data mining. The properties of big data raise higher demand for more efficient and economical distributed clustering methods. However, existing distributed clustering methods mainly…

Hierarchical clustering is a stronger extension of one of today's most influential unsupervised learning methods: clustering. The goal of this method is to create a hierarchy of clusters, thus constructing cluster evolutionary history and…

Real-world networks often come with side information that can help to improve the performance of network analysis tasks such as clustering. Despite a large number of empirical and theoretical studies conducted on network clustering methods…

We introduce the use of hierarchical clustering for relaxed, deterministic coordination and control of multiple robots. Traditionally an unsupervised learning method, hierarchical clustering offers a formalism for identifying and…

In this work we focus on efficient heuristics for solving a class of stochastic planning problems that arise in a variety of business, investment, and industrial applications. The problem is best described in terms of future buy and sell…

Hierarchical clustering is a popular unsupervised data analysis method. For many real-world applications, we would like to exploit prior information about the data that imposes constraints on the clustering hierarchy, and is not captured by…