The clustering of cryptocurrencies - as an emerging field in investment management - is the main topic of this research. Applying the information-based distance matrices, we clustered the 30 most valuable cryptocurrencies. Then, we identified the most influential clustering by the concept of Minimum Spanning Tree (MST) and the centrality measures of graph theory. A second-order clustering, which is defined as the clustering of hierarchical clusterings, is applied to cluster 56 dendrograms. Using the most influential clustering, we identified the main clusters of cryptocurrencies and sub-clusters. The results show that the clustering composition of cryptocurrencies changed at the period I (before COVID-19) and II (pandemic time).
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.
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.
Riri Fitri Sari, Asri Samsiar Ilmananda, Daniela M. Romano
In the current digital era, information exchanges can be done easily through the Internet and social media. However, the actual truth of the news on social media platforms is hard to prove, and social media platforms are susceptible to the spreading of hoaxes. As a remedy, Blockchain technology can be used to ensure the reliability of shared information and can create a trusted communications environment. In this study, we propose a social media news spreading model by adapting an epidemic methodology and a scale-free network. A Blockchain-based news verification system is implemented to identify the credibility of the news and its sources. The effectiveness of the model is investigated by utilizing agent-based modelling using NetLogo software. In the simulations, fake news with a truth level of 20% are assigned a low News Credibility Indicator (NCI ± -0.637) value for all of the different network dimensions. Moreover, the Producer Reputation Credit is also decreased (PRC ± 0.213) so that the trust factor value is reduced. Our epidemic approach for news verification has also been implemented using Ethereum Smart Contract and several tools such as React with Solidity, IPFS, Web3.js, and Metamask. By showing the measurements of the credibility indicator and reputation credit to the user during the news dissemination process, this proposed smart contract can effectively limit user behaviour in spreading fake news and improve the content quality on social media.
Ignacio Amores-Sesar, Christian Cachin, Anna Parker
Despite the tremendous interest in cryptocurrencies like Bitcoin and Ethereum today, many aspects of the underlying consensus protocols are poorly understood. Therefore, the search for protocols that improve either throughput or security (or both) continues. Bitcoin always selects the longest chain (i.e., the one with most work). Forks may occur when two miners extend the same block simultaneously, and the frequency of forks depends on how fast blocks are propagated in the network. In the GHOST protocol, used by Ethereum, all blocks involved in the fork contribute to the security. However, the greedy chain selection rule of GHOST does not consider the full information available in the block tree, which has led to some concerns about its security.
Timothy Tzen Vun Yap, Ting Fong Ho, Hu Ng, Vik Tor Goh
<ns3:p> <ns3:bold>Background:</ns3:bold> This research uses exploratory graph analysis to analyze the transaction data of the Ethereum network. This is achieved through network visualization and mathematical and statistical modelling of the network data. </ns3:p> <ns3:p> <ns3:bold>Methods:</ns3:bold> The dataset used in this study was extracted from the Ethereum in the BigQuery public dataset, specifically selected transactions in July 2019. The transactions were firstly modelled as network graphs and then visualized using the Kamada-Kawai and force-directed graphs layouts. Further modelling was explored with classical random graph and network block, with emphasis on network cohesion, hierarchical clustering and community membership. </ns3:p> <ns3:p> <ns3:bold>Results:</ns3:bold> Looking at the network visualization and hierarchical clustering of the data, the network shows 170 clusters, the largest having 135 members. Through random graph modelling the optimum number of clusters is shown to be 95. Referring to the generated dendrograms, notable large transactions center around the DRINK token, the Maximine Exchange, the Upbit2 Exchange and the IDEX Exchange, identified through public disclosure of their Ethereum addresses. The network graphs tend to go towards the DRINK smart contract and the Maximine Exchange, indicating deposit actions, while it is the opposite for the IDEX Exchange. Further analysis also shows a different number of communities than the expected number. Falling short of the expected 170 clusters, the model is not able to capture additional mechanism that may be present at the density and social interaction distribution level of the network. On the other hand, network block modelling shows only four major clusters out of the 170 expected clusters, an indication that the model is not able to capture the network sufficiently. </ns3:p> <ns3:p> <ns3:bold>Conclusions:</ns3:bold> The study was able to capture and model the interconnectedness of the system with its notion of elements, in this case, the transactions on the network. </ns3:p>
Blockchain is a digital technology built on three pillars: decentralization, transparency and immutability. Bitcoin and Ethereum are two prevalent Blockchain platforms, where the participants are globally connected in a peer-to-peer manner and anonymously perform trade electronically. The vast number of decentralized transactions and the pseudo-anonymity of participants open the door for scams, cyber frauds, hacks, money laundering and fraudulent transactions. It is challenging to detect such fraudulent activities using traditional auditing techniques, since they need more processing power, time and memory for complex queries to join combinations of tables. This paper proposes several algorithms to extract the transaction- related features from the Bitcoin and Ethereum networks and to represent the features as graphs. Moreover, the paper discusses how visualisation of graphs can reflect the anomalies and patterns of fraudulent activities.
In this paper, the cross-correlations of cryptocurrency returns are analysed. The paper examines one years worth of data for 146 cryptocurrencies from the period January 1 2019 to December 31 2019. The cross-correlations of these returns are firstly analysed by comparing eigenvalues and eigenvector components of the cross-correlation matrix C with Random Matrix Theory (RMT) assumptions. Results show that C deviates from these assumptions indicating that C contains genuine information about the correlations between the different cryptocurrencies. From here, Louvain community detection method is applied as a clustering mechanism and 15 community groupings are detected. Finally, PCA is completed on the standardised returns of each of these clusters to create a portfolio of cryptocurrencies for investment. This method selects a portfolio which contains a number of high value coins when compared back against their market ranking in the same year. In the interest of assessing continuity of the initial results, the method is also applied to a smaller dataset of the top 50 cryptocurrencies across three time periods of T = 125 days, which produces similar results. The results obtained in this paper show that these methods could be useful for constructing a portfolio of optimally performing cryptocurrencies.
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.
Aug 19, 2021·2021 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)
Shiv Sondhi, Sherif Saad, Kevin Shi, Mohammad Abdullah Al Mamun · 5 authors
A critical component of any blockchain or distributed ledger technology (DLT) platform is the consensus algorithm. Blockchain consensus algorithms are the primary vehicle for the nodes within a blockchain network to reach an agreement. In recent years, many blockchain consensus algorithms have been proposed mainly for private and permissioned blockchain networks. However, the performance of these algorithms and their reliability in hostile environments or the presence of byzantine and other network failures are not well understood. In addition, the testing and validation of blockchain applications come with many technical challenges. In this paper, we apply chaos engineering and testing to understand the performance of consensus algorithms in the presence of different loads, byzantine failure and other communication failure scenarios. We apply chaos engineering to evaluate the performance of three different consensus algorithms (PBFT, Clique, Raft) and their respective blockchain platforms. We measure the blockchain network's throughput, latency, and success rate while executing chaos and load tests. We develop lightweight blockchain applications to execute our test in a semi-production environment. Our results show that using chaos engineering helps understand how different consensus algorithms perform in a hostile or unreliable environment and the limitations of blockchain platforms. Our work demonstrates the benefits of using chaos engineering in testing complex distributed systems such as blockchain networks.
Applications on blockchain are currently limited by the relatively poor performance of the blockchain network such as low TPS, high latency and the resulting high transaction fees. Hence, performance optimization is one crucial problem of blockchain. Focusing on the most prosperous public blockchain Ethereum, we model the on-chain transaction confirmation process with the knowledge of Poisson process and queueing theory, derives the mean transaction-confirmation time, and explores the effect of different transaction fees on latency. We also conduct a numeric simulation of the model, which indicates that the model fits in well with the real world blockchain.
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.
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 rigorously evaluated or optimized. In this paper, we tackle several challenges of change address identification and clustering. First, we build a ground truth set of transactions with known change from the Bitcoin blockchain that can be used to validate the efficacy of individual change address detection heuristics. Equipped with this data set, we develop new techniques to predict change outputs with low false positive rates. After applying our prediction model to the Bitcoin blockchain, we analyze the resulting clustering and develop ways to detect and prevent cluster collapse. Finally, we assess the impact our enhanced clustering has on two exemplary applications.
Much of the current research in Ethereum transaction records focuses on the statistical analysis and measurements of existing data; however, the evolution mechanism of Ethereum transactions is an important, yet seldom discussed issue. In this work, we first collect the transaction data of Ethereum and build network models from a microlevel view and then use a link-prediction-based framework to quantify the impact of network characteristics on Ethereum evolution. Next, we explore the graph structure properties and the driving factors of newly generated transaction relationships. Experimental results show that the local and microscopic structure of Ethereum networks is star-shaped, and the transaction frequency of addresses has a great impact on the evolution of Ethereum transaction relationships. First-layer nodes of microstructures dominate the network evolution. Moreover, the degree of addresses is an effective basis for predicting the direction of new transactions. Potential further studies on Ethereum transaction link prediction are discussed, for example, the label effect of center addresses.
Summary Ethereum is arguably the second most popular cryptocurrency‐based network after Bitcoin. Both use the distributed ledger technology known as the blockchain, which is considered secure. However, the provided security level is proportional to the number of connected nodes, the number of influential nodes, and the supported amount of hash power. Thus, the knowledge of the network properties and nodes' behavior is helpful to protect the network from possible attacks such as double‐spending attacks, DDoS attacks, 51% attacks, and Sybil attacks. This paper proposes a node discovery mechanism, which performs a P2P link discovery on the Ethereum main network. For that, we develop Search‐node, a modified version of Ethereum client that searches for all participating nodes in the blockchain network, stores the node information in the Bucket, and then processes the peer discovery method. Based on the collected data, we first visualize the Ethereum network topology and analyze the attributes of the network such as node degree, path length, diameter, and clustering coefficient. We then analyze the node properties and provide analytical results regarding the relationship between nodes, heavily connected nodes, node geo‐distribution, security issues, and possible attacks over the influential nodes. As a result, we have identified 68,406 nodes with a total of 642,034 edges. By analyzing the collected data, we have found that the diameter in the Ethereum network is equal to 8. The node degree is over 19, which is two times higher than the default configuration.
Blockchain data has become a popular subject in studying various aspects of blockchains including the security of underlying mechanisms. However, the main chain block data, usually available from block explorer services, does not serve as a sufficient source of transaction and block dynamics that are only visible from a large-scale event measurement. In this paper, the transaction and block arrival events of the two popular public blockchains, i.e., Bitcoin and Ethereum, are measured to investigate the hidden dynamics of blockchain networks. We share our key findings and security implications including a false universal assumption of previous mining related studies and an invalid transaction propagation problem that can be exploited to launch a Denial-of-Service attack on a network.
As the pioneer of blockchain technology, Bitcoin is the most famous cryptocurrency to date. Given its dramatic price swings along with constant news and events, the dynamics of cryptocurrencies are difficult to quantify. These dynamics are believed to be affected by various political, security, financial, and regulatory events. This paper presents the Impact-Score metric, an efficient framework that attempts to quantify the impact of events on the Bitcoin blockchain. We have applied our framework to 16 major worldwide events and the Bitcoin blockchain network (defined as Bitcoin transaction and users, blockchain data, and memory pool data) from 2016-2018. The results show that the majority of the events are correlated with quantifiable changes in the Bitcoin blockchain and network. We also observed correlations between event types (e.g. financial events) and event subtypes on certain Bitcoin blockchain network temporal features.
Andrea Lisi, Damiano Di Francesco Maesa, Paolo Mori, Laura Ricci
The Lightning Network is a P2P overlay that allows two Bitcoin users to create a new payment channel with a transaction, and later exchange cryptocurrency on that channel independently from the Bitcoin blockchain, improving Bitcoin’s scalability issues and making it more suitable for frequent micropayments. Moreover, the channels can also be used to route payments between two users who do not share a direct channel themselves.In this paper we study the topology of the Lightning Network in a given time period, performing a number of analysis on the channels and on the nodes composing the network. We study the churn rate, we evaluate centrality measurements and the clustering coefficient to evaluate the network connectivity, and finally we analyze the presence of a pattern that we named "bouquet". The importance of this pattern is proven by the finding that, removing specific nodes of the "bouquets" (1% of the total) causes the disconnection from the largest component of about 41% nodes.
The blockchain consensus algorithm can be leveraged to enhance IoT security and promote the efficiency of collaboration among IoT nodes. Delegated Proof of Stake (DPoS) can simultaneously meet the low-cost and high-efficiency requirements and improve the service quality of node collaboration. However, the malicious attacks, selfishness and insufficient enthusiasm of collaborative nodes can affect DPoS consensus process. In view of these challenges, we modify DPoS consensus algorithm, and design a self-selecting abnormal data detection algorithm to identify abnormal data from malicious attacks and selfishness behavior of nodes. Meanwhile, a game theory-based reward and punishment incentive mechanism is proposed to improve the voting enthusiasm of nodes. Simulation results show that the proposed mechanism can effectively reduce the effect of malicious attacks and nodes selfishness in DPoS consensus process.
Jan A. Fischer, Andres Palechor, Daniele Dell’Aglio, Abraham Bernstein · 5 authors
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 transaction is composed of a set of input addresses (associated with unspent outputs received from previous transactions) and a set of output addresses (to which Bitcoins are transferred). Despite Bitcoin was designed with anonymity in mind, different heuristic approaches exist to detect which addresses in a specific transaction belong to the same entity. By applying these heuristics, we build an Address Correspondence Network: in this representation, addresses are nodes are connected with edges if at least one heuristic detects them as belonging to the same entity. %addresses are nodes and edges are drawn between addresses detected as belonging to the same entity by at least one heuristic. %nodes represent addresses and edges model the likelihood that two nodes belong to the same entity %In this network, connected components represent sets of addresses controlled by the same entity. In this paper, we analyse for the first time the Address Correspondence Network and show it is characterised by a complex topology, signalled by a broad, skewed degree distribution and a power-law component size distribution. Using a large-scale dataset of addresses for which the controlling entities are known, we show that a combination of external data coupled with standard community detection algorithms can reliably identify entities. The complex nature of the Address Correspondence Network reveals that usage patterns of individual entities create statistical regularities; and that these regularities can be leveraged to more accurately identify entities and gain a deeper understanding of the Bitcoin economy as a whole.
Jan A. Fischer, Andres Palechor, Daniele Dell’Aglio, Abraham Bernstein · 5 authors
Bitcoin is built on a blockchain, an immutable decentralised ledger that\nallows entities (users) to exchange Bitcoins in a pseudonymous manner. Bitcoins\nare associated with alpha-numeric addresses and are transferred via\ntransactions. Each transaction is composed of a set of input addresses\n(associated with unspent outputs received from previous transactions) and a set\nof output addresses (to which Bitcoins are transferred). Despite Bitcoin was\ndesigned with anonymity in mind, different heuristic approaches exist to detect\nwhich addresses in a specific transaction belong to the same entity. By\napplying these heuristics, we build an Address Correspondence Network: in this\nrepresentation, addresses are nodes are connected with edges if at least one\nheuristic detects them as belonging to the same entity. %addresses are nodes\nand edges are drawn between addresses detected as belonging to the same entity\nby at least one heuristic. %nodes represent addresses and edges model the\nlikelihood that two nodes belong to the same entity %In this network, connected\ncomponents represent sets of addresses controlled by the same entity. In this\npaper, we analyse for the first time the Address Correspondence Network and\nshow it is characterised by a complex topology, signalled by a broad, skewed\ndegree distribution and a power-law component size distribution. Using a\nlarge-scale dataset of addresses for which the controlling entities are known,\nwe show that a combination of external data coupled with standard community\ndetection algorithms can reliably identify entities. The complex nature of the\nAddress Correspondence Network reveals that usage patterns of individual\nentities create statistical regularities; and that these regularities can be\nleveraged to more accurately identify entities and gain a deeper understanding\nof the Bitcoin economy as a whole.\n