In this work, we consider community detection in blockchain networks. We specifically take the Bitcoin network and Ethereum network as two examples, where community detection serves in different ways. For the Bitcoin network, we modify the traditional community detection method and apply it to the transaction social network to cluster users with similar characteristics. For the Ethereum network, on the other hand, we define a bipartite social graph based on the smart contract transactions. A novel community detection algorithm which is designed for low-rank signals on graph can help find users' communities based on user-token subscription. Based on these results, two strategies are devised to deliver on-chain advertisements to those users in the same community. We implement the proposed algorithms on real data. By adopting the modified clustering algorithm, the community results in the Bitcoin network is basically consistent with the ground-truth of betting site community which has been announced to the public. At the meanwhile, we run the proposed strategy on real Ethereum data, visualize the results and implement an advertisement delivery on the Ropsten test net.
Online social networks (OSN) are becoming more important in people's daily life, however, all popular OSNs are centralized, and this raises a series of security, privacy and management issues. A decentralized architecture based on blockchain technology provides the ability to solve above issues. In this paper, an OSN service is developed based on blockchain technology in order to make it operate decentralized. Large volume of data normally required low-security requirements can be stored in Interplanetary Filesystem (IPFS) to make data decentralized. A decentralized autonomous organization is developed for user autonomy, users can self-manage the OSN in a democratic way.
Human behavior as they engaged in financial activities is intimately connected to the observed market dynamics. Despite many existing theories and studies on the fundamental motivations of the behavior of humans in financial systems, there is still limited empirical deduction of the behavioral compositions of the financial agents from a detailed market analysis. Blockchain technology has provided an avenue for the latter investigation with its voluminous data and its transparency of financial transactions. It has enabled us to perform empirical inference on the behavioral patterns of users in the market, which we explore in the bitcoin and ethereum cryptocurrency markets. In our study, we first determine various properties of the bitcoin and ethereum users by a temporal complex network analysis. After which, we develop methodology by combining k -means clustering and Support Vector Machines to derive behavioral types of users in the two cryptocurrency markets. Interestingly, we found four distinct strategies that are common in both markets: optimists, pessimists, positive traders and negative traders. The composition of user behavior is remarkably different between the bitcoin and ethereum market during periods of local price fluctuations and large systemic events. We observe that bitcoin (ethereum) users tend to take a short-term (long-term) view of the market during the local events. For the large systemic events, ethereum (bitcoin) users are found to consistently display a greater sense of pessimism (optimism) towards the future of the market.
Besides all uncommon events of 2020, Bitcoin finally passed 20,000 USD in this year, and grabbed more attention about what is going on cryptocurrency and what will happen next? So far, several researchers used social media data in their works and the role of public opinion especially in the specialized forums on Bitcoin price was proved.
Jan 1, 2021·Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences
This paper demonstrates the use of network analysis to identify core nodes associated with ransomware attacks in cryptocurrency transaction networks. The method helps trace the cyber entities involved in cryptocurrency attacks and supports intelligence efforts to identify and disrupt cryptocurrency networks. A data corpus is built by the unsupervised machine learning graph algorithm ‘DeepWalk’ [1]. DeepWalk evaluates the position of nodes within networks. It compares the relative position of different nodes (similarity) and identifies those whose removal would most affect the network (riskiness). This method helps identify on the blockchain the key nodes that are involved in the execution of a ransomware attack. When applied to the ransomware “cash out” graph, the method derived “riskiness” scores for specific nodes. Analysing the derived “riskiness” at a community level (groups of nodes in the network) provides an enhanced granularity for identifying and targeting influential nodes. Such insight could potentially support both intelligence and forensics investigations.
Andrii Bielinskyi, Oleksandr Serdyuk, Сергій Олексійович Семеріков, Vladimir Soloviev
Cryptocurrencies refer to a type of digital asset that uses distributed ledger, or blockchain technology to enable a secure transaction. Like other financial assets, they show signs of complex systems built from a large number of nonlinearly interacting constituents, which exhibits collective behavior and, due to an exchange of energy or information with the environment, can easily modify its internal structure and patterns of activity. We review the econophysics analysis methods and models adopted in or invented for financial time series and their subtle properties, which are applicable to time series in other disciplines. Quantitative measures of complexity have been proposed, classified, and adapted to the cryptocurrency market. Their behavior in the face of critical events and known cryptocurrency market crashes has been analyzed. It has been shown that most of these measures behave characteristically in the periods preceding the critical event. Therefore, it is possible to build indicators-precursors of crisis phenomena in the cryptocurrency market.
Blockchain and general purpose distributed ledgers are foundational technologies which bring significant innovation in the infrastructures and other underpinnings of our socio-economic systems. These P2P technologies are able to securely diffuse information within and across networks, without need for trustees or central authorities to enforce consensus. In this contribution, we propose a minimalistic stochastic model to understand the dynamics of blockchain-based consensus. By leveraging on random-walk theory, we model block propagation delay on different network topologies and provide a classification of blockchain systems in terms of two emergent properties. Firstly, we identify two performing regimes: a functional regime corresponding to an optimal system function; and a non-functional regime characterised by a congested or branched state of sub-optimal blockchains. Secondly, we discover a phase transition during the emergence of consensus and numerically investigate the corresponding critical point. Our results provide important insights into the consensus mechanism and sub-optimal states in decentralised systems.
Consensus mechanism plays an important role in blockchain. At present, mainstream consensus mechanisms include proof of work (PoW), proof of stake (PoS), and delegated proof of stake (DPoS). PoW, as is widely used in virtual currency, results in significant energy consumption; PoS and DPoS are proposed to reduce energy waste caused by PoW, but their disadvantage is that they tend to create Matthew Effect (ME): “the rich get richer.” In order to balance the discourse power of new nodes and elder ones, this paper proposes a flexible consensus mechanism called proof of engagement (PoE), based on the activity and contribution of network nodes. We analyze the incentive compatibility of PoE from the perspective of mechanism design. In our simulation experiments, we tested the profit changes under PoW, PoS, and PoE. The results illustrate it is easier for new nodes to accumulate their profits under PoE than under PoW or PoS, so as to reduce the negative impacts of ME.
With the wide application of blockchain in the financial field, the rise of various types of cybercrimes has brought great challenges to the security of blockchain. In order to better understand this emerging market and explore more efficient countermeasures for effective supervision, it is imperative to track transactions on blockchain-based systems. Due to the openness of Ethereum, we can easily access the publicly available transaction records, model them as a complex network, and further study the problem of transaction tracking via link prediction, which provides a deeper understanding of Ethereum transactions from a network perspective. Specifically, we introduce an embedding based link prediction framework that is composed of temporal-amount snapshot multigraph (TASMG) and present temporal-amount walk (TAW). By taking the realistic rules and features of transaction networks into consideration, we propose TASMG to model Ethereum transaction records as a temporal-amount network and then present TAW to effectively embed accounts via their transaction records, which integrates temporal and amount information of the proposed network. Experimental results demonstrate the superiority of the proposed framework in learning more informative representations and could be an effective method for transaction tracking.
In this paper, we provide detailed analyses of the Bitcoin network and its main participants. We build a novel database using a large number of public and proprietary sources to link Bitcoin addresses to real entities and develop an extensive suite of algorithms to extract information about the behavior of the main market participants. We conduct three major pieces of analysis of the Bitcoin eco-system. First, we analyze the transaction volume and network structure of the main participants on the blockchain. Second, we document the concentration and regional composition of the miners which are the backbone of the verification protocol and ensure the integrity of the blockchain ledger. Finally, we analyze the ownership concentration of the largest holders of Bitcoin.
A major concern of the adoption and scalability of Blockchain technologies refers to their efficient use for payments. In this work, we analyze how Lightning Network (LN), which represents a relevant infrastructural novelty, is influenced by the market dynamics of its referring cryptocurrency, namely Bitcoin. In so doing, we focus on how the LN is efficient in performing transactions and we relate this feature to the market conditions of Bitcoin. By applying the Toda–Yamamoto variant of Granger-causality, we note that market conditions of Bitcoin do not significantly influence the topological configuration of the LN. Hence, although the LN represents a second layer on the Bitcoin blockchain, our findings suggest that its efficient functioning does not appear to be related to the simple market performance of its underlying cryptocurrency and, in particular, of its volatile market fluctuations. This result may therefore contribute to shed light on the practical usage of the LN as a blockchain technology to favor transactions.
In this study, we conduct a network analysis with centrality measures, using historical daily close prices of top 120 cryptocurrencies between 2013 and 2020, to study and understand the dynamic evolution and characteristics of the cryptocurrency market. Our study has two primary findings: (1) the overall cross-return correlation among the cryptocurrencies is weakening from 2013 to 2016 and then strengthening thereafter; (2) cryptocurrencies that are primarily used for transaction payment, notably BTC, dominate the market until mid-2016, followed by those developed for applications using blockchain as the underlying technology, particularly data storage and recording such as MAID and FCT, between mid-2016 and mid-2017. Since then, ETH, alongside with its strongly correlated cryptocurrencies have replaced BTC to become the benchmark cryptocurrencies. Furthermore, during COVID-19, QTUM and BNB have intermittently replaced ETH to take the leading positions due to their active community engagement during the pandemic.
Kamyar Sabri‐Laghaie, Saeid Jafarzadeh Ghoushchi, Fatemeh Elhambakhsh, Abbas Mardani
A completely new economic system is required for the era of Industry 4.0. Blockchain technology and blockchain cryptocurrencies are the best means to confront this new trustless economy. Millions of smart devices are able to complete transparent financial transactions via blockchain technology and its related cryptocurrencies. However, via blockchain technology, internet-connected devices may be hacked to mine cryptocurrencies. In this regard, monitoring the network of these blockchain-based transactions can be very useful to detect the abnormal behavior of users of these cryptocurrencies. Therefore, the trustworthiness of the transactions can be assured. In this paper, a novel procedure is proposed to monitor the network of blockchain cryptocurrency transactions. To do so, a hidden Markov multi-linear tensor model (HMTM) is utilized to model the transactions among nodes of the blockchain network. Then, a multivariate exponentially weighted moving average (MEWMA) control chart is applied to the monitoring of the latent effects. Average run length (ARL) is used to evaluate the performance of the MEWMA control chart in detecting blockchain network anomalies. The proposed procedure is applied to a real dataset of Bitcoin transactions.
Blockchain is a decentralized database, which can protect the safety of trade and avoid double payment. Due to the widespread attention of researchers, the studies of this field have increased sharply in recent years. It is meaningful to reveal the development level and trends based on this literature. This paper adopts bibliometric methods to study the collaboration characteristics from the levels of author, institution and country. Furthermore, several kinds of collaboration networks and their centrality analysis are also presented, which not only display the development level and collaboration degree but also the evolution of author collaboration modes in different phases.
As one of the most important and famous applications of blockchain technology, cryptocurrency has attracted extensive attention recently. Empowered by blockchain technology, all the transaction records of cryptocurrencies are irreversible and recorded in the blocks. These transaction records containing rich information and complete traces of financial activities are publicly accessible, thus providing researchers with unprecedented opportunities for data mining and knowledge discovery in this area. Networks are a general language for describing interacting systems in the real world, and a considerable part of existing work on cryptocurrency transactions is studied from a network perspective. This survey aims to analyze and summarize the existing literature on analyzing and understanding cryptocurrency transactions from a network perspective. Aiming to provide a systematic guideline for researchers and engineers, we present the background information of cryptocurrency transaction network analysis and review existing research in terms of three aspects, i.e., network modeling, network profiling, and network-based detection. For each aspect, we introduce the research issues, summarize the methods, and discuss the results and findings given in the literature. Furthermore, we present the main challenges and several future directions in this area.
Abstract In this work, the issue of predicting the edge weight in Bitcoin network has been addressed by leveraging community structure that involves members who trust with each other in their transactions. The proposed model consists of two main stages; the first one is the detection of trusted Bitcoin communities by implementing Newman- Girvan algorithm. In the context, the attributes of node have been modeling in different ways to get different structure of communities each time. Secondly, prediction the missing edge weight based on the neighbors of edge-source in community. In other words, the trust values that pointed to edge-target by neighbors are averaged to represent the prediction of missing edge weight. Practically, the model has been evaluated using two real-world datasets; Bitcoin-OTC and Bitcoin-Alpha datasets. The experimental results explicate the effectiveness of the proposed model comparable with other methods, where the minimization percentage for Bitcoin-OTC dataset is 4% and 18% for all and partial edges respectively. As for Bitcoin-Alpha dataset are 0% and 30% for all and partial edges respectively.
Bryan Wolff, Diego Fernandes Gonçalves Martins, Marco Aurélio Amaral Henriques
A blockchain é um sistema de registro seguro e distribuído que necessita de um mecanismo de consenso capaz de definir quais novos blocos são inseridos à cadeia, a medida que são produzidos pelos participantes da rede. A análise da evolução de uma blockchain controlada por um novo mecanismo de consenso pode ser complexa. Para simplificar tal análise, este artigo apresenta o desenvolvimento de uma nova ferramenta para monitoramento da evolução dinâmica de uma blockchain. É apresentada em detalhes a arquitetura da mesma e sua integração com o protocolo Probabilistic Proof-of-Stake (PPoS).
We study the growth of a time-ordered rooted tree by probabilistic attachment of new vertices to leaves. We construct a likelihood function of the leaves based on the connectivity of the tree. We take such connectivity to be induced by the merging of directed ordered paths from leaves to the root. Combining the likelihood with an assigned prior distribution leads to a posterior leaf distribution from which we sample attachment points for new vertices. We present computational examples of such Bayesian tree growth. Although the discussion is generic, the initial motivation for the paper is the concept of a distributed ledger, which may be regarded as a time-ordered random tree that grows by probabilistic leaf attachment.
Stanisław Drożdż, Jarosław Kwapień, Paweł Oświȩcimka, Tomasz Stanisz · 5 authors
Social systems are characterized by an enormous network of connections and factors that can influence the structure and dynamics of these systems. Among them the whole economical sphere of human activity seems to be the most interrelated and complex. All financial markets, including the youngest one, the cryptocurrency market, belong to this sphere. The complexity of the cryptocurrency market can be studied from different perspectives. First, the dynamics of the cryptocurrency exchange rates to other cryptocurrencies and fiat currencies can be studied and quantified by means of multifractal formalism. Second, coupling and decoupling of the cryptocurrencies and the conventional assets can be investigated with the advanced cross-correlation analyses based on fractal analysis. Third, an internal structure of the cryptocurrency market can also be a subject of analysis that exploits, for example, a network representation of the market. In this work, we approach the subject from all three perspectives based on data from a recent time interval between January 2019 and June 2020. This period includes the peculiar time of the Covid-19 pandemic; therefore, we pay particular attention to this event and investigate how strong its impact on the structure and dynamics of the market was. Besides, the studied data covers a few other significant events like double bull and bear phases in 2019. We show that, throughout the considered interval, the exchange rate returns were multifractal with intermittent signatures of bifractality that can be associated with the most volatile periods of the market dynamics like a bull market onset in April 2019 and the Covid-19 outburst in March 2020. The topology of a minimal spanning tree representation of the market also used to alter during these events from a distributed type without any dominant node to a highly centralized type with a dominating hub of USDT. However, the MST topology during the pandemic differs in some details from other volatile periods.
Franklin Tchakounté, Koudanbe Amadou Calvin, Ado Adamou Abba Ari, David Jaurès Fotsa-Mbogne
One of the main concerns of cybersecurity is the detection of hoaxes across social media. Hoaxers propagate such messages to mislead users and to promote violence. Several approaches exist in literature to address this issue. They are mainly limited to detect hoax activities by characterizing the message nature and detecting provenance of messages. However, unless hoaxes are detected, they continue to propagate across social media nodes. This work aims at reducing the dissemination of hoaxes across group of users. Relying on social graph structure, this research develops a mechanism based on smart contract logics to prevent a group to consume a fake post. To achieve this objective, we used a smart contract to exploit a trust index computed based on message characteristics and group features such as graph density, group status, group degree, group acceptability. Based on the value of trust index, the message is forwarded or blocked. Experiments realized on groups of different characteristics revealed that the proposed smart contract is even able to reactively block a fake post of the same nature than the group type. Results indicate that the proportion of targeted groups could be reduced even if their interests match with the message subject. This research is an important step forward to anti-promote hoaxes with the novelty of exploiting smart contract approach to contain their propagation.
Kristina Kapanova, Barbara Guidi, Andrea Michienzi, Kevin Koidl
Online Social Networking platforms (OSNs) are part of the people's everyday life answering the deep-rooted need for communication among humans. During recent years, a new generation of social media based on blockchain became very popular, bringing the power of the technology to the service of social networks. Steemit is one such and employs the blockchain to implement a rewarding mechanism, adding a new, economic, layer to the social media service. The reward mechanism grants virtual tokens to the users capable of engaging other users on the platform, which can be either vested in the platform for increased influence or exchanged for fiat currency. The introduction of an economic layer on a social networking platform can seriously influence how people socialize. In this work, we tackle the problem of understanding how this new business model conditions the way people create contents. We performed term frequency and topic modelling analyses over the written contents published on the platforms between 2017 and 2019. This analysis lets us understand the most common topics of the contents that appear in the platform. While personal mundane information still appears, along with contents related to arts, food, travels, and sport, we also see emerging a very strong presence of contents about blockchain, cryptocurrency and, more specifically, on Steemit itself and its users.
There is abundantly documented scientific evidence that the financial transactions that have grown rapidly recently, in conjuction with the interest of the public, were due to the sharp rise in the price of Bitcoin in December 2017. As a consequence, a freshly emerging dataset in the research community has emerged. Therefore, the aim of the present investigation was to examine the analyses of data in this newly emerging dataset in the research community. In order to achieve the extraction of data, their conversion to network and finally their fragmentation, the studied variables were analyzed by using two parts of analysis, namely, statistical network analyses and economic activity analyses. Network statistical analyses was employed aiming to analyze, in a holistic approach, the complex systems of modern times which are represented as networks, as it is impossible to analyze them partially, in order to avoid incorrect conclusions. Additionally, the analyses of economic activity, which is related to indicators from the stock market and the economics of science, was used, after it had been transferred and matched with the economic model represented by Bitcoin. The results distinguished the extent of the data generated by the statistical analyses of the networks and the analyses of economic activity. With respect to data presented, we established that the daily transaction networks were scale free networks which were not evolving like ER random networks and they were not defined as the small world. Also, it was demonstrated that daily transaction networks cannot be reproduced in a random way like ER random networks. Furthermore, the opportunities and problems encountered in conducting the present research were briefly presented.
Blockchain is a distributed ledger technology for recording transactions. To guarantee the immutability of the blocks, miners need a lot of computational resources for generating blocks (mining). Since mining is conducted distributedly by many miners, chain forks can occur in the blockchain. In this paper, we investigate the effects of miners’ locations, the size of the mining pool (MP), and the network structure on the selection rate of blocks generated by the MP when chain forks occur through simulations.