Cryptocurrencies Activity as a Complex Network: Analysis of Transactions
Graphs
- URL: http://arxiv.org/abs/2110.14765v1
- Date: Tue, 14 Sep 2021 08:32:36 GMT
- Title: Cryptocurrencies Activity as a Complex Network: Analysis of Transactions
Graphs
- Authors: Luca Serena, Stefano Ferretti, Gabriele D'Angelo
- Abstract summary: 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.
- Score: 7.58432869763351
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The number of users approaching the world of cryptocurrencies exploded in the
last years, and consequently the daily interactions on their underlying
distributed ledgers have intensified. In this paper, we analyze the flow of
these digital transactions in a certain period of time, trying to discover
important insights on the typical use of these technologies by studying,
through complex network theory, the patterns of interactions in four prominent
and different Distributed Ledger Technologies (DLTs), namely Bitcoin, DogeCoin,
Ethereum, Ripple. In particular, we describe the Distributed Ledger Network
Analyzer (DiLeNA), a software tool for the investigation of the transactions
network recorded in DLTs. We show that studying the network characteristics and
peculiarities is of paramount importance, in order to understand how users
interact in the DLT. For instance, our analyses reveal that all transaction
graphs exhibit small world properties.
Related papers
- LasTGL: An Industrial Framework for Large-Scale Temporal Graph Learning [61.4707298969173]
We introduce LasTGL, an industrial framework that integrates unified and unified implementations of common temporal graph learning algorithms.
LasTGL provides comprehensive temporal graph datasets, TGNN models and utilities along with well-documented tutorials.
arXiv Detail & Related papers (2023-11-28T08:45:37Z) - Live Graph Lab: Towards Open, Dynamic and Real Transaction Graphs with
NFT [28.08921595650609]
We introduce the concept of it Live Graph Lab for temporal graphs, which enables open, dynamic and real transaction graphs from blockchains.
We instantiate a live graph with NFT transaction network and investigate its dynamics to provide new observations and insights.
arXiv Detail & Related papers (2023-10-18T04:54:22Z) - 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) - Graph Regularized Nonnegative Latent Factor Analysis Model for Temporal
Link Prediction in Cryptocurrency Transaction Networks [1.6801544027052142]
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.
arXiv Detail & Related papers (2022-08-03T08:58:59Z) - 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) - Evidential Temporal-aware Graph-based Social Event Detection via
Dempster-Shafer Theory [76.4580340399321]
We propose ETGNN, a novel Evidential Temporal-aware Graph Neural Network.
We construct view-specific graphs whose nodes are the texts and edges are determined by several types of shared elements respectively.
Considering the view-specific uncertainty, the representations of all views are converted into mass functions through evidential deep learning (EDL) neural networks.
arXiv Detail & Related papers (2022-05-24T16:22:40Z) - Deep Architecture Connectivity Matters for Its Convergence: A
Fine-Grained Analysis [94.64007376939735]
We theoretically characterize the impact of connectivity patterns on the convergence of deep neural networks (DNNs) under gradient descent training.
We show that by a simple filtration on "unpromising" connectivity patterns, we can trim down the number of models to evaluate.
arXiv Detail & Related papers (2022-05-11T17:43:54Z) - Temporal Graph Network Embedding with Causal Anonymous Walks
Representations [54.05212871508062]
We propose a novel approach for dynamic network representation learning based on Temporal Graph Network.
For evaluation, we provide a benchmark pipeline for the evaluation of temporal network embeddings.
We show the applicability and superior performance of our model in the real-world downstream graph machine learning task provided by one of the top European banks.
arXiv Detail & Related papers (2021-08-19T15:39:52Z) - 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) - Knowledge Discovery in Cryptocurrency Transactions: A Survey [1.2744523252873352]
This article analyzes and summarizes the existing research on knowledge discovery in the cryptocurrency transactions using data mining techniques.
For each aspect, we present the problems, summarize the methodologies, and discuss major findings in the literature.
An enumeration of transaction data parsing and visualization tools and services is also provided.
arXiv Detail & Related papers (2020-10-02T14:38:08Z) - Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph
Link Prediction [69.1473775184952]
We introduce a realistic problem of few-shot out-of-graph link prediction.
We tackle this problem with a novel transductive meta-learning framework.
We validate our model on multiple benchmark datasets for knowledge graph completion and drug-drug interaction prediction.
arXiv Detail & Related papers (2020-06-11T17:42:46Z)
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.