Dimitar Kitanovski, Miroslav Mirchev, Ivan Chorbev, Igor Mishkovski
As of the end of 2013 till now we are witnessing huge volatility and risk in the cryptocurrency market compared to flat currency or stock market. Thus, in this market the portfolio diversification is of big importance in order to reduce volatility and keep the optimal return for the investors. A usual approach for portfolio construction is to keep a balance between returns and volatility, based on their interdependence and individual returns. One way of diversification is employing clustering or community detection algorithms to select a more diverse set of assets. We study the utilization of the Louvain algorithm and affinity propagation for community detection, based on correlation and mutual information between cryptocurrencies, for potential application in portfolio diversification.
Javier Arroyo, David Davó, Elena Martínez-Vicente, Youssef Faqir-Rhazoui · 5 authors
Decentralized Autonomous Organizations (DAOs) are a new kind of organization that relies on blockchain software to govern their projects. Typically, DAO members may put forward and vote on proposals. For instance these proposals may consist on someone doing some tasks in exchange for a share of the DAO crypto-funds. In recent times, DAOs have gained a remarkable adoption, and yet they are still understudied by the academic literature. In this work, we present a visual analytics tool to study DAO activity focusing on their participation and temporal evolution. Our tool will hopefully help to stimulate research on this new kind of online community and collaborative software.
Blockchain technology has given rise to a series of new blockchain online social media (BOSMs), of which Steemit is representative. Such communities are based on a token reward system and attempt to engross users in the knowledge activities of the community through knowledge payment. Studies have found that the reward system of such communities has been abused (e.g., collusion for profit), but few studies have performed an in-depth analysis for this phenomenon. Consequently, real data for Steemit are used as a case study herein to examine the collusion of users in BOSMs. Two user collusion behaviors (group-voting and vote-buying) are defined and measured. On this basis, an identification and evolutionary survival analysis of the two collusion behaviors are conducted for colluding users and colluding groups, and the behavior patterns of user collusion under the token system are deconstructed. The results of this study improve stakeholders’ understanding of user participation behavior in new online communities, and serve as a reference for decision-making in community governance and token design.
Community detection is essential in P2P network analysis as it helps identify connectivity structure, undesired centralization, and influential nodes. Existing methods primarily utilize topological data and neglect the rich content data. This paper proposes a technique combining topological and content data to detect communities inside the Bitcoin network using a deep feature representation algorithm and Deep Feedforward Autoencoders. Our results show that the Bitcoin network has a higher clustering coefficient, assortativity coefficient, and community structure than expected from a random P2P network. In the Bitcoin network, nodes prefer to connect to other nodes that share the same characteristics.
Distributed Ledger Technology (DLT), commonly referred to as blockchains, provide a new approach to security. Rather than securing items by blocking access, data is made immutable by creating a massively distributed data structure. Entries are secured using cryptographic primitives. Coordination is done using consensus algorithms. We start by introducing random graph theory and presenting its major result: the emergence of a giant component. We explain how and when giant components arise. We explain why the existence, or not, of giant components result in the presence, or not, of graph properties of interest. We then introduce the most widely used consensus algorithms, concentrating on potential problems. We then describe a simple light weight mining (LWM) alternative. We use Erdös's random graph theory to prove the effectiveness and efficiency of LWM. This is an example of how random graph theory can be used to design and implement robust DLT systems
The skyrocketing market value of cryptocurrencies has prompted more investors to pour funds into cryptocurrencies to seek asset hedging. However, the anonymity of blockchain makes cryptocurrency naturally a tool of choice for criminals to commit smart contract scams. Consequently, smart contract scam detection is particularly critical for investors to avoid economic loss. Previous methods mainly leverage specific code logic of smart contracts and/or design rules based on abnormal transaction behaviors for scam detection. Although these methods gain success at detecting particular scams, they perform worse when applied to scams with highly similar codes. Besides, well-designed decision rules rely on expert knowledge and tedious data collection steps, which causes poor flexibility. To combat these challenges, we consider the problem of smart contract scam detection via mining topological features of account interaction information that dynamically evolves. We adopt interactive features extracted from dynamic interaction information of accounts and propose a framework named TTG-SCSD to utilize the features and Topological Data Analysis for smart contract scams detection. The TTG-SCSD constructs discrete dynamic interaction graphs for each contract and designs interactive features that characterize account behaviors. The features are modeled combined with a topology quantification mechanism to capture contract intentions in transactions. Experimental results on real-world transaction datasets from Ethereum show that TTG-SCSD obtains better generalizability and improves the performance of the bare versions of the comparison methods.
Abstract Event studies in general rely on having a high-quality curated database of events. In this paper we introduce CryptoGDelt2022, a news event dataset extracted from the Global Database of Events, Language and Tone (GDELT) containing more than 243 thousands cryptocurrency related news events between the 31st of March 2021 and 30th of April 2022. The dataset is enriched with supervised machine learning scores for Relevance, Sentiment and Strength. Supervised Relevance Score measures how related to Cryptocurrency the topic is using news web scrapped from Yahoo in general and from the Cryptocurrency part of the site, after a comparison of approaches; Latent Dirichlet Allocation (LDA), BERT and Naive Bayes, Naive Bayes was chosen and the hyper-parameter tuned model reached accuracy: 97.84 % in the train set and 91.70% in the test set. Supervised Sentiment Score measures the negative, neutral or positive tone of the news, after hyper-parameter tuning, the retrained FinBERT model achieved accuracy of 92.63% in the train set and 86.11% in the test set. Supervised Strength Score measures how strong the news by using the abnormal return using Fama French 3-factor model as target output variable, after hyper-parameter tuning, the trained Naive Bayes model reached accuracy of 63.34%. The work concludes that GDELT is more reliable source of event when compared to news selected from cryptocurrency specialized websites as it presents a more balanced positive and negative number of news. All data sets and Python Jupyter Notebooks are available in the project's GitHub.
Web3, one of the novel paradigms which may drive the evolution of the future Web, is offering an invaluable volume of data stored in the supporting blockchains. Researchers from different fields such as network science, computational social science and data mining, might benefit from these large collections of temporal and heterogeneous data capturing different kinds of interaction among people and between people and the platforms. In this study we focus on a specific issue related to these modern techno-social systems, i.e. the understanding of the rules driving their growth. To reach this goal, we performed an analysis based on graph evolution rules - GERs - on different networks gathered from Web3 platforms such as Steemit or OpenSea. Graph evolution rules mining is a frequency-based method for evaluating network evolution which does not require any prior growth process for disentangling how networks evolve. By comparing the evolution rules of social network platforms and asset trading services through GER profiles, we observe that some evolution rules are common to all Web3 platforms, regardless of the system specificity. On the other hand, in specific cases, the frequency of graph evolution rules is influenced by the nature of the platform: whereas social and token-transfer networks are characterized by rules which increase network transitivity and reciprocity, NFT trading networks, especially those specialized in a specific type of digital asset, are driven by rules which form trading chains. These findings suggest that the GER approach and the GER profiles are a good starting point to get insights into the evolutionary behavior of a network and to define a classification of graph evolution rules.
Bishenghui Tao, Hong‐Ning Dai, Haoran Xie, Fu Lee Wang
Both the metaverse and its underlying blockchain technology have attracted extensive attention in the past few years. It becomes a natural problem to extract, process, and analyze the tremendous data generated by the blockchain systems for various metaverse applications though it also poses diverse challenges. Amongst those challenges, this paper mainly focuses on modeling and understanding the blockchain transaction network from a structural identity perspective, which represents the entire network structure and reveals the relations among multiple entities. In this paper, we propose a novel representation learning method named Structure-to-Vector with Random Pace (SVRP) for learning both latent representation and structural identity of blockchain transaction networks. We then conduct node classification and link prediction tasks with integration with Graph Neural Networks (GNNs). Empirical results on three representative blockchain data sets, namely Non-fungible token (NFT), Ethereum (ETH), and Bitcoin (BTC), demonstrate that our proposed SVRP outperforms other existing methods in multiple tasks. In particular, our SVRP achieves the highest node classification accuracy (99.3%) while only requiring original non-attributed graphs (i.e., graphs without node features).
Marco Alberto Javarone, Gabriele Di Antonio, Gianni Valerio Vinci, Raffaele Cristodaro · 6 authors
Abstract The behaviour of Bitcoin owners is reflected in the structure and the number of bitcoin transactions encoded in the Blockchain. Likewise, the behaviour of Bitcoin traders is reflected in the formation of bullish and bearish trends in the crypto market. In light of these observations, we wonder if human behaviour underlies some relationship between the Blockchain and the crypto market. To address this question, we map the Blockchain to a spin-lattice problem, whose configurations form ordered and disordered patterns, representing the behaviour of Bitcoin owners. This novel approach allows us to obtain time series suitable to detect a causal relationship between the dynamics of the Blockchain and market trends of the Bitcoin and to find that disordered patterns in the Blockchain precede Bitcoin panic selling. Our results suggest that human behaviour underlying Blockchain evolution and the crypto market brings out a fascinating connection between disorder and panic in Bitcoin dynamics.
Thang Tat Nguyen, Hoang-Nam Dinh, Van-Thanh Nguyen, Do Bao Son · 6 authors
Recently, blockchain technology has been applied in many domains in our life. Blockchain networks typically utilize a consensus protocol to achieve consistency among network nodes in a decentralized environment. Delegated Proof of Stake (DPoS) is a popular mechanism adopted in many networks such as BitShares, EOS, and Cardano because of its speed and scalability advantages. However, votes that come from nodes on a DPoS network tend to support a set of specific nodes that have a greater chance of becoming block producers after voting rounds. Therefore, only a small group of nodes can be selected to become block producers. To address this issue, we propose a new protocol called Evolutionary Computation-based Proof of Criteria (ECPoC), which uses ten criteria to evaluate and select a new block procedure in each round. Next, a set of optimal weights used for maximizing the network’s decentralization level is identified through the use of evolutionary computation algorithms. The experimental results show that our consensus significantly enhances the degree of decentralization in the selection process of witness nodes compared to DPoS. As a result, ECPoC facilitates fairness between nodes and creates momentum for blockchain network development
Fengyang Guo, Xun Xiao, Artur Hecker, Schahram Dustdar
IOTA blockchain system is lightweight without heavy proof-of-work mining phases, which is considered a promising service platform of Internet of Things applications. IOTA organizes ledger data in a directed acyclic graph (DAG), called Tangle, rather a chain structure as in traditional blockchains. With arriving messages, IOTA tangle grows in a special way, as multiple messages can be attached to the tangle at different locations in parallel. Hence, the network dynamics of an operational IOTA system would justify a thorough study, which is currently unexplored in the literature. In this article, we present the first theoretical modeling for the evolving IOTA tangle based on stochastic analysis. After analyzing snapshots of the real-world IOTA ledger data, our key finding suggests that IOTA tangle follows a rather atypical double Pareto Lognormal (dPLN) degree distribution. In contrast, typical power-law and exponential distributions do not accurately reflect the fact. For model parameter estimation, we further realize that using generic optimization solvers cannot yield quality fitting results. Thus, we design an alternative algorithm based on expectation-maximization (EM) framework. We evaluate the proposed model and fitting algorithm with official data provided by the IOTA Foundation. Quantitative comparisons confirm the fitting quality of our proposed model and algorithm. The whole analysis reveals a deeper understanding of the internal mechanism of the IOTA network.
Abstract Nowadays, blockchain is an upcoming area for researchers from different research fields. Bitcoin, as the first successful cryptocurrency, has accumulated numerous data after its existence. Here, the Bitcoin transaction graph from a graph theory perspective is investigated with more available data given now. This paper mainly focuses on the transaction graph and provides researchers with both practical and theoretical sides of the data. Several existent measurements and some newer ones are first computed and analysed. These measurements help to interpret the transaction graph more extensively. A new modified Buckley–Osthus random graph model is proposed, and a simulation of the Chung–Lu model is attempted to represent the Bitcoin transaction network. Some suggestions are given to improve the modified Buckley–Osthus model and point out the pros and cons of these random graph models. Moreover, the experiments show that scale‐free networks are fundamentally not a good model for Bitcoin transaction networks considering all the data, but the mechanics of preferential attachment is crucial. How to proceed with Bitcoin transaction graph theory from both theoretical and experimental perspectives for future studies is also discussed and analysed.
Decentralized cryptocurrency systems have become primary targets for attackers due to substantial profit gain and economic rewards. A number of attack models have been proposed during last few years. However, the evaluation and comparison of those attack models remain problematic due to the lack of systematic framework to analyze them. In this work, we propose a general quantitative analysis framework for attack models in the network and consensus layer of blockchain. We identify the problem statement and evolution process. And we show how to apply our general framework in previous attacks such as selfish mining and bribery attack. We also explained that the framework is suitable for other attacks in blockchain. For further exploration, we simulate the success rate and benefits of different attacks through experiments. We provide several defensive strategies, and study how these strategies against previous attack models.
We have built a bare-metal testbed in order to perform large-scale, reproducible evaluations of erasure coding algorithms. Our testbed supports at least 1000 Ethereum Swarm peers running on 30 machines. Running experimental evaluation is time-consuming and challenging. Researchers must consider the experimental software's limitations and artifacts. If not controlled, the network behavior may cause inaccurate measurements. This paper shares the lessons learned from a bare-metal evaluation of erasure coding algorithms and how to create a controlled-environment in a cluster consisting of 1000 Ethereum Swarm peers.
Bitcoins framework (also known as Bitcoins cryptocurrency) is a distributed and decentralized cryptocurrency installment framework that utilizes cryptographic money called bitcoins (BTCs) and was delivered as free and open-source as well as free to use programming in the year 2009 [1]. Bitcoins stage has drawn in both several users which can be categorized as social and against social (anti-social or illegal) components. To see and study how the social and against social inclinations in the client community of Bitcoins influence its development, there is an urgent need to break down the Bitcoins framework as an organization. The current research paper plans to investigate the neighborhood geography and math of the Bitcoins cryptocurrency network as it completes its first decade of presence and achieves nearly a trillion dollar brand value. Bitcoins exchange information was handled for this reason to fabricate a Bitcoins client chart. The qualities, neighborhood and worldwide organization properties of the client’s chart were investigated at ten spans. Little measurement, slanted circulation of exchanges, power-law dispersed in and out degrees, detached chart, and availability of enormous associated parts were the perceptions from network or graph examination.
With the development of blockchain technology, a cryptocurrency based on blockchain technology is becoming more and more popular. The huge cryptocurrency transaction network has therefore received widespread attention. The link prediction learning structure of the network is supportive to understand the mechanism of networks, so it also has been widely studied in the cryptocurrency network. However, the dynamics of cryptocurrency transaction networks have been neglected in past studies. In this study, therefore, we use a graph-regularized method to link past transaction records with future transactions. Based on this, we propose a single latent factor-dependent, nonnegative, multiplicative, and graph regularized-incorporated update (SLF-NMGRU) algorithm and further propose a graph regularized nonnegative latent factor analysis (GrNLFA) model. Eventually, the experimental results on a real cryptocurrency transaction network show that the proposed method improves both the accuracy and computational efficiency.
Min-Yuh Day, Pei-Tz Chiu, Yung-Wei Teng, Chao-Lin Liu
Anti-money laundering involving cryptocurrencies has become a popular research topic in recent years. Moreover, constructing a knowledge graph of cryptocurrency anti-money laundering in a small sample of judgments to prevent cryptocurrency money laundering has become an essential issue for an improved understanding of the relationship between crime patterns and emerging financial technologies. The research method of this study is that we conducted a named entity recognition task and identified the key relation types to construct a cryptocurrency anti-money laundering knowledge graph (KG). Accordingly, we developed the “Judicia17,” a key relation type for cryptocurrency anti-money laundering KG. The contribution of this study is that the proposed “Judica17” relation types of cryptocurrency anti-money laundering KG can be applied to construct a legal knowledge graph.
Blockchain's technological characteristics, such as decentralization, robustness, and anti-modification, represent a significant challenge to the regulation of existing networks and data security. Effective regulation of blockchain applications is one of the keys to maintain the healthy and sustainable development of the blockchain ecology. We analyze the merits and demerits of the existing technologies in the research directions of tracking and visualization of blockchain nodes, consortium blockchain penetration regulation technology, public chain active discovery and exploration disposition techniques, and chain governance. In the end, we present the future research directions in respect of blockchain traceability and compliance regulation.
Ethereum, currently the most actively-used and the second-largest blockchain platform, consists of a heterogeneous ecosystem, cohabited by human users, smart contracts (autonomous agents), ether (native cryptocurrency), tokens (digital assets), dApps (decentralized applications), and DeFi (decentralized finance). These key actors in the Ethereum interact with each other via transactions and contract calls. Given the highly connected structure, graph-based modeling is an optimal tool to analyze the data stored in Ethereum blockchain. Recently, several research works performed graph analysis on the publicly available Ethereum blockchain data to reveal insights into its transactions and for important downstream tasks, e.g., cryptocurrency price prediction, address clustering, phishing scams and counterfeit tokens detection. In this work, we conduct an in-depth survey of the existing literature. We categorize them based on publication years, venues, core ranking, and authors' affiliations, data usage and graphs construction, graph mining and machine learning techniques employed, and the new insights derived by them. We conclude by discussing our recommendations on the future work. Our article will be useful to data scientists, researchers, financial analysts, and blockchain enthusiasts.