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151 results · page 3 of 7
Peilin He, Tao Yan, Chuanshan Huang, Nicolò Vallarano · 5 authors
No abstract is available for this record.
Tingting Liu, Min Liu, Qiang Guo, Jian-Guo Liu
The collective behaviors of community members in dynamic bitcoin transaction network are significant to understand the evolutionary characteristics of communities for bitcoin transaction network. In this paper, we empirically investigate the behavior evolution of new nodes forming communities for the bitcoin transaction network. First, we divide the bitcoin transaction network into multiple time segments, and detect community on each time segment. Then, according to the set similarity method, we mark the community with maximal similarity [Formula: see text] at adjacent timestamps as the new community. Finally, we propose an evolution index to illustrate the evolution trend of new nodes forming communities, and introduce the reshuffle model to compare with it. The results show that there are obvious differences in the early stage, and new traders tend to join new communities. However, after August 2011, the trends of before and after reorganization are very similar, which indicates that in bitcoin trading, the behaviors of new traders forming communities become random. Our work may be helpful for the understanding of user behavior characteristics in bitcoin trading, and provide a new perspective for the research of bitcoin transaction network.
Amir Mohammad Karimi Mamaghan, Amin Setayesh, Behnam Bahrak
Bitcoin and Ethereum are the two most used decentralized blockchains. These platforms use a notion of addresses to represent identities in the network. These addresses are publicly visible entities that tell where funds are sent and received on a blockchain. Each user can have multiple addresses in a cryptocurrency network. In this paper, we investigate the lifespan of addresses in these networks, in particular, the lifespan distribution and its relationship with the other features such as turnover, turnover in USD, and transaction count. Our results show that addresses' lifespans follow a Double Pareto-Lognormal (DPLN) distribution. We also showed the relationship between lifespan and turnover, turnover in USD, and transaction count follows a power-law distribution.
Hongting Tang, Jian Ni, Yanlin Zhang
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.
Marco Alberto Javarone, Gabriele Di Antonio, Gianni Valerio Vinci, L. Pietronero · 5 authors
The energy sustainability of blockchains, whose consensus protocol rests on the Proof-of-Work, nourishes a heated debate. The underlying issue lies in a highly energy-consuming process, defined as mining, required to validate crypto-asset transactions. Mining is the process of solving a cryptographic puzzle, incentivised by the possibility of gaining a reward. The higher the number of users performing mining, i.e. miners, the higher the overall electricity consumption of a blockchain. For that reason, mining constitutes a negative environmental externality. Here, we study whether miners' interests can meet the collective need to curb energy consumption. To this end, we introduce the Crypto-Asset Game, namely a model based on the framework of Evolutionary Game Theory devised for studying the dynamics of a population whose agents can play as crypto-asset users or as miners. The energy consumption of mining impacts the payoff of both strategies, representing a direct cost for miners and an environmental factor for crypto-asset users. The proposed model, studied via numerical simulations, shows that, in some conditions, the agent population can reach a strategy profile that optimises global energy consumption, i.e. composed of a low density of miners. To conclude, can a Proof-of-Work-based blockchain become energetically sustainable? Our results suggest that blockchain protocol parameters could have a relevant role in the global energy consumption of this technology.
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.
Runyu Chen, Lunwen Wang, Rangang Zhu
The Delegated Proof of Stake (DPoS) consensus mechanism uses the power of stakeholders to not only vote in a fair and democratic way to solve a consensus problem, but also reduce resource waste to a certain extent. However, the fixed number of member nodes and single voting type will affect the security of the whole system. In order to reduce the negative impact of the above problems, a new consensus algorithm based on vague set and node impact factors is proposed. We first use fuzzy values to calculate the ratings of all nodes and initially determine the number of agent nodes according to the preset threshold value. Then, we judge whether a secondary screening is needed. If needed, calculating the nodes' impact factor based on their neighboring nodes, and combining their impact factors with adjacency votes to further distinguish the nodes with the same fuzzy value. In addition, we analyze the dynamic changes in the composition and scale of the agent node set and give its ideal size through testing. Finally, we compare the proposed algorithm with DPoS algorithm and existing fuzzy set-based algorithms in different scales and network structures. Results show that no matter in what kind of network structures, the effectiveness of the proposed algorithm is improved. Among which, the most noticeable improvement is seen in complex network structures.
Antonio Briola, Tomaso Aste
We investigate logarithmic price returns cross-correlations at different time horizons for a set of 25 liquid cryptocurrencies traded on the FTX digital currency exchange. We study how the structure of the Minimum Spanning Tree (MST) and the Triangulated Maximally Filtered Graph (TMFG) evolve from high (15 s) to low (1 day) frequency time resolutions. For each horizon, we test the stability, statistical significance and economic meaningfulness of the networks. Results give a deep insight into the evolutionary process of the time dependent hierarchical organization of the system under analysis. A decrease in correlation between pairs of cryptocurrencies is observed for finer time sampling resolutions. A growing structure emerges for coarser ones, highlighting multiple changes in the hierarchical reference role played by mainstream cryptocurrencies. This effect is studied both in its pairwise realizations and intra-sector ones.
Victor Chang, Karl Hall, Qianwen Xu, Le Minh Thao Doan · 5 authors
This paper applies social network analysis in two experiments. In the first experiment, social network analysis is conducted on student friendship networks to find relational patterns. Then, three community detection methods are used to divide the student network. The RSiena package is used to illustrate the coevolution of friendship networks with smoking and drinking behavior. In this experiment, it was determined that in the closed network, same-sex reciprocated relationships are preferred. The second experiment analyzes a weighted trust network that involves users trading with Bitcoin on the BTC-Alpha platform. Since the dealers of Bitcoin are anonymous, there is an urgent need to record every dealer’s credit history to prevent fraud and other security problems. The second experiment aims to improve security problems within the Bitcoin trust network by applying social network analysis.
Zhiyong Zheng, Yunfan Lu, Junhuan Zhang
No abstract is available for this record.
Yan Zhao, Sheng Bin, Gengxin Sun
With the development of blockchain technology, many new social networks based on blockchain technology have emerged. The unique consensus mechanism and incentive mechanism of blockchain technology makes the law of information propagation in the new social network different from that in the traditional social network. Based on the information propagation characteristics of blockchain social network, this paper considers the influence of opposing groups of opinions, incentive mechanism and user’s conformity psychology in blockchain social network, and uses the evolutionary game to define the transfer process and probability between states and puts forward a new information propagation model. This paper analyses the influence of group density, state transition probability, and incentive policy on information transmission trends in the network through simulation experiments. The comparative experiment with the traditional model shows that the model in this paper can describe the propagation behaviour choices of different propagators under different incentive policies, which the traditional model cannot describe. Using the model in this paper to analyse the information propagation of blockchain social networks can effectively inhibit the propagation of inferior information and further build a good network public opinion environment.
Rui Dias, Nicole Horta, Catarina Revez, Paula Heliodoro · 5 authors
When compared to traditional financial markets, cryptocurrencies were seen as assets with minimal correlations. However, because this continually expanding financial market is marked by substantial volatility and strong price movements over a short period, developing an accurate and reliable forecasting model is deemed crucial for portfolio management and optimization. Given the relevance of cryptocurrencies in the global economy, it is important to determine if Bitcoin (BTC) becomes more predictable as investors adopt more aggressive trading positions. We examine BTC over the period from May 15th, 2021, to April 14th, 2022 (8676-time data), using intraday (hourly) time scales. The results reveal that the random walk hypothesis is rejected at lags of 3 to 16 days, while we see that the BTC market tends toward efficiency (see the evolution between lags of 16 and 2). These findings reveal that, given the uncertainty in the global economy in 2022, namely the Russian invasion of Ukraine, the BTC market shows values of the variance ratios close to unity, implying that it is, apparently, not predictable and that the residuals are not autocorrelated in time. In addition, the results of the Detrended Fluctuation Analysis (DFA) exponent show that this market does not exhibit characteristics of (in) efficiency in its weak form. In other words, this market does not have persistent and mean-reverting properties, thus validating the results of Wright’s Rankings and Signs variance test.
Victor Chang, Karl Hall, Qianwen Xu, Le Minh Thao Doan · 5 authors
No abstract is available for this record.
Wenpin Tang, David D. Yao
We propose and study a new class of polynomial voting rules for a general decentralized decision/consensus system, and more specifically for the proof-of-stake protocol. The main idea, inspired by the Penrose square-root law and the more recent quadratic voting rule, is to differentiate a voter’s voting power and the voter’s share (fraction of the total in the system). We show that, whereas voter shares form a martingale process that converges to a Dirichlet distribution, their voting powers follow a supermartingale process that decays to zero over time. This prevents any voter from controlling the voting process and, thus, enhances security. For both limiting results, we also provide explicit rates of convergence. When the initial total volume of votes (or stakes) is large, we show a phase transition in share stability (or the lack thereof), corresponding to the voter’s initial share relative to the total. We also study the scenario in which trading (of votes/stakes) among the voters is allowed and quantify the level of risk sensitivity (or risk aversion) in three categories, corresponding to the voter’s utility being a supermartingale, a submartingale, and a martingale. For each category, we identify the voter’s best strategy in terms of participation and trading. Funding: W. Tang gratefully acknowledges financial support through the National Science Foundation [Grants DMS-2113779 and DMS-2206038] and through a start-up grant at Columbia University. D. D. Yao’s work is part of a Columbia–City University/Hong Kong collaborative project that is supported by InnoHK Initiative, the Government of Hong Kong Special Administrative Region, and the Laboratory for AI-Powered Financial Technologies.
Wenpin Tang
This paper is concerned with the stability of shares in a cryptocurrency where the new coins are issued according to the Proof of Stake protocol. We identify large, medium and small investors under various rewarding schemes, and show that the limiting behaviors of these investors are different -- for large investors their shares are stable, while for medium to small investors their shares may be volatile or even shrink to zero. For instance, with a geometric reward there is chaotic centralization, where all the shares will eventually concentrate on one investor in a random manner. This leads to the phase transition phenomenon, and the thresholds for stability are characterized. In response to the increasing activities in blockchain networks, we also propose and analyze a dynamical population model for the PoS protocol, which allows the number of investors to grow over the time. Numerical experiments are provided to corroborate our theory.
Barbara Guidi, Andrea Michienzi
Surfing Online Social Media (OSM) websites have become a daily activity for a large number of people worldwide. People use OSMs to satisfy their innate need to socialise, but also as a source of information or to share personal facts. Thanks to the massive success of cryptocurrencies, the blockchain technology gained popularity among researchers, giving birth to a new generation of social media. Steemit is the most well-known blockchain-based social media, and it is based on the public blockchain Steem. Steemit employs Steem as data storage, and to implement a rewarding mechanism that grants cryptocurrency to pieces of content that are considered relevant by the users. Steem represents the first experiment that integrates OSMs and an economic rewarding system on the same platform, and in this paper, we inspect the interactions among the users from a community perspective. We apply two community detection algorithms on five graphs that model just as many facets of the Steem blockchain and test the detected structure against three measures for community structure evaluation. Findings show that communities tend to be very large, index of how much users are encouraged to interact as much as possible, and in particular, in the monetary graph, we detect a large number of the block producers of Steem.
Rahul Saha, Gulshan Kumar, Alessandro Brighente, Mauro Conti
Reputation plays a fundamental role in the blockchain ecosystem, as it provides means to assign trust levels to participating nodes. Reputation is usually gained in time: the higher the fair participation of a node to the network, the higher its reputation. IOTA’s Coordicide proposes a reputation system based on an asset named Mana. This reputation system is the base of the Coordicide’s primary security protection. At present, IOTA assigns to nodes an amount of Mana equivalent of the amount of transferred funds only. However, this may lead to privacy leakage on nodes’ status and may not be sufficient to fully characterize the behavior of a node in the network. Furthermore, the Mana reputation system is currently susceptible to monopoly, eclipse, and spamming attacks. An efficient and secure reputation system shall comprehend a larger number of factors to provide security and reliability.In this paper, we propose Enhanced Mana (eMana), a theoretical model for the IOTA reputation system accounting for nodes’ good or bad behaviour based on their assigned tasks. We model each of the Coordicide modules having direct or indirect connections with the reputation system. In eMana the behavior of a peer node can be transparently analyzed by the other peers; thus, it is infeasible to modify the reputation value. Theoretically, eMana is flexible enough to include possible new modules’ behaviour and provides a strict hard-to-gain reputation. To the best of our knowledge, eMana is the first mathematical model for Coordicide’s enhanced reputation based on nodes’ multi-dimensional behaviour. Additionally, eMana is generic and flexible, and may also be adaptable to other Distributed Ledger Technology (DLT) systems.
Celina Jepsen Færch, Poon Sze Sen, Tian Yunqian
Abstract This study investigates the information flow and social organization in a Bitcoin discussion network on Twitter (BDN), using social network analysis to examine user‐user interactions. Results suggest that BDN presents a heterogenous degree distribution, where most users have few interactions. A myriad of weakly defined but well‐connected subcommunities enables efficient information flow in the network. BDN emanates a small‐world effect, without the dominance of a few influential users. “Star‐power agents,” such as Elon Musk, are popular references among users, but the network is driven bottom‐up by smaller “two‐sided” users. This explorative study demonstrates how the classification of different users can be used in analyzing online communities. Future research could compare the network structure of BDN with other networks and utilize sentiment analysis to analyze the quality of information.
Jinke Geng, Yi Li, Fang Li, Ping Chen
Exploring the evolution of the transaction network is very important for analyzing anonymous transaction behavior of encrypted currency and avoiding illegal crimes. However, with the rapid growth of cryptocurrency transactions in recent years, the traditional static analysis models tend to ignore parameter changes in the evolution process. To solve this problem, constructing time-varying network model is an effective scheme. The main challenge of turning the massive statically stored transaction data into dynamical sequential network is to design a set of suitable network model. This paper took Bitcoin system as example, combined complex network evolution theory, proposed a weighted variable directed bipartite graph (VW-DBG) model. Initially, we defined the transaction weights and the influence inditcator of nodes, and accordingly introduced the node deletion mechanism to Bitcoin transaction network analysis for the first time. Moreover, information entropy indicators was defined to screen key periods in the evolution. In addition, we analyzed the dynamic and static indicators of real Bitcoin transaction network under different observation time using this model, and revealed the pow-law distribution in evolutionary networks.
Chun-Xiao Nie
No abstract is available for this record.
Cheick Tidiane Bâ, Matteo Zignani, Sabrina Gaito
The rising of online social platforms makes large volumes of data about social relationships and interactions available to the research community. In the varied ecosystem of techno-social platforms, blockchain-based online social networks - BOSNs - are gaining momentum since the underlying blockchain offers data validation, data storage, and data decentralization. As data sources, BOSNs provide high-resolution temporal data about the evolution of the social network and on the interactions of users with the platform services. In this study, we focus on a few temporal characteristics, by analyzing the dynamics of the link creation process and the claiming of rewards in the BOSN Steemit. We model blockchain data as a temporal directed network from which we extract the time series characterizing link creation and reward claims. Adopting a user-centric approach, we evaluate the heterogeneity of the time series through the inter-event time distribution, the burstiness, the bursty train size distribution, and the fitting of inter-event times by power law models. The outcomes of the analysis highlight that the above processes show bursty traits typical of human dynamics. However, the two aspects present a few differences concerning the types of models describing their behavior and the time scale of their bursty nature. To sum up, the creation of new relationships and the reward claim dynamics ask for specific models able to reproduce their general bursty traits but taking into account their specificities and relations with other services and mechanisms offered by BOSN platforms.
Kwansoo Kim, Nabyla Daidj
International audience
Fan Yang, Yanan Qiao, Shan Wang, Cheng Huang · 5 authors
No abstract is available for this record.
Yunjie Chen, Zhihai Rong
In this paper, we investigated the evolution of user behavior on Ethereum through building directed and weighted external owned accounts trading networks (DWETN) from August 10th, 2015 to June 9th, 2017. It is showed that the evolution of the structural properties of the DWETN and important events are closely related. The relationship between the number of users and the total number of transactions is linear, and each user will have 1.5 transactions on average. Out-degree, in-degree, out-strength, and in-strength distributions follow the power-law distributions, and the power exponent values of out-degree and in-degree distributions change much more than out-strength and in-strength distributions, which implies that, despite the changes in network structure, the transaction behavior pattern of users have inherent stability. The evolution of directed degree correlation coefficients and directed and weighted Pearson correlation coefficients of the network indicates that in-degree(in-strength) of the source node of an edge has a weak effect on the target node's in-degree(in-strength) and out-degree(out-strength), and as time evolves, the effect of out-degree(out-strength) of the source node of an edge on the target node's in-degree(in-strength) and out-degree(out-strength) from negative to weak.