Beibei Han, Yingmei Wei, Qingyong Wang, Francesco Maria De Collibus · 5 authors
Abstract In recent years, a surge of criminal activities with cross-cryptocurrency trades have emerged in Ethereum, the second-largest public blockchain platform. Most of the existing anomaly detection methods utilize the traditional machine learning with feature engineering or graph representation learning technique to capture the information in transaction network. However, these methods either ignore the timestamp information and the transaction flow direction information in transaction network or only consider single transaction network, the cross-cryptocurrency trading patterns in Ethereum are usually ignored. In this paper, we introduce a Multi-layer Temporal Transaction Anomaly Detection (MT $$^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow/> <mml:mn>2</mml:mn> </mml:msup> </mml:math> AD) model in Ethereum network with graph neural network. Specifically, for a given Ethereum token transaction network, we first extract its initial features including the structure subgraph and edge’s feature. Then, we model the temporal information in subgraph as a series of network snapshots according to the timestamp on each edge and time window. To capture the cross-cryptocurrency trading patterns, we combine the snapshots from multiple token transactions at a given timestamp, and we consider it as a new combined graph. We further use the graph convolution encoder with attention mechanism and pooling operation on this new graph to obtain the graph-level embedding, and we transform the anomaly detection on dynamic multi-layer Ethereum transaction networks as a graph classification task with these graph-level embeddings. MT $$^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow/> <mml:mn>2</mml:mn> </mml:msup> </mml:math> AD can integrate the transaction structure feature, edge’s feature and cross-cryptocurrency trading patterns into a framework to perform the anomaly detection with graph neural networks. Experiments on three real-world multi-layer transaction networks show that the proposed MT $$^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow/> <mml:mn>2</mml:mn> </mml:msup> </mml:math> AD (0.8789 Precision, 0.9375 Recall, 0.4987 FbMacro and 0.9351 FbWeighted) can achieve the best performance on most evaluation metrics in comparison with some competing approaches, and the effectiveness in consideration of multiple tokens is also demonstrated.
Nils Augustin, Andreas Eckhardt, Alexander Willem de Jong
Abstract Blockchain technology is argued to drastically change the way we operate within an organizational context, with decentralized autonomous organizations (DAOs) representing a first manifestation of this ongoing trend. DAOs are characterized by an online community that builds the organization’s backbone by providing knowledge and human resources in a transparent, virtual manner, as well as the use of blockchain technology to coordinate their endeavor. Nevertheless, current research highlights the conceptual ambiguity of this emerging phenomenon, leading to potential issues for practitioners and researchers. To provide further clarity on the phenomenon, we study DAOs through the perspective of their members with a two-staged approach by combining elements of a netnographic approach and structural topic modeling. Our findings highlight several contextual features surrounding DAOs, such as their members’ underlying beliefs and views, helping to embed DAOs in existing research streams.
In recent years, the rapid growth of blockchain technology has sparked massive curiosity and transformed various industries. Among the numerous blockchain platforms, Ethereum has gathered significant attention for its decentralized applications and smart contracts. Understanding Ethereum and its network interactions is a challenging task but with various methods at our disposal, such as graph embeddings, we gain valuable insight into its operations. Graph embeddings are powerful techniques in the realm of data representation, which have become a focal point in analyzing complex structures. By capturing the essence of graph’s structure and semantics, embeddings enable efficient analysis of vast networks. In the context of blockchain-induced networks, graph embeddings offer effective insights into the behavior and dynamics of transactions and addresses. In my Bachelor’s thesis, conducted under the guidance and support of Dr. Ferenc Beres and Marcell Nagy, I aim to explore the connection between graph embeddings and blockchain-induced networks from Ethereum with a binary classification problem on certain Ethereum accounts’ network interactions as graphs. Additionally, I analyze graph-level properties and employ dimensionality reduction techniques to visualize these networks in the embedding space.
Community detection is widely used in social networks to uncover groups of related vertices (nodes). In cryptocurrency transaction networks, community detection can help identify users that are most related to known illegal users. However, there are challenges in applying community detection in cryptocurrency transaction networks: (1) the use of pseudonymous addresses that are not directly linked to personal information make it difficult to interpret the detected communities; (2) on Bitcoin, a user usually owns multiple Bitcoin addresses, and nodes in transaction networks do not always represent users. Existing works on cluster analysis on Bitcoin transaction networks focus on addressing the later using different heuristics to cluster addresses that are controlled by the same user. This research focuses on illegal community detection containing one or more illegal Bitcoin addresses. We first investigate the structure of Bitcoin transaction networks and suitable community detection methods, then collect a set of illegal addresses and use them to label the detected communities. The results show that 0.06% of communities from daily transaction networks contain one or more illegal addresses when 2,313,344 illegal addresses are used to label the communities. The results also show that distance-based clustering methods and other methods depending on them, such as network representation learning, are not suitable for Bitcoin transaction networks while community quality optimization and label-propagation-based methods are the most suitable.
Nicolò Vallarano, Tiziano Squartini, Claudio J. Tessone
The open availability of the entire history of the Bitcoin transactions opens up the possibility to study this system at an unprecedented level of detail. This contribution is devoted to the analysis of the mesoscale structural properties of the Bitcoin User Network (BUN), across its entire history (i.e. from 2009 to 2017). What emerges from our analysis is that the BUN is characterized by a core-periphery structure a deeper analysis of which reveals a certain degree of bow-tieness (i.e. the presence of a Strongly-Connected Component, an IN- and an OUT-component together with some tendrils attached to the IN-component). Interestingly, the evolution of the BUN structural organization experiences fluctuations that seem to be correlated with the presence of bubbles, i.e. periods of price surge and decline observed throughout the entire Bitcoin history: our results, thus, further confirm the interplay between structural quantities and price movements observed in previous analyses.
Because of its ability to completely revamp current blockchain security methods, this connection is crucial. An effective safeguard against complex assaults, Nonlinear Dynamics (ND) adds a living, breathing component to consensus methods and cryptographic primitives. There is an urgent need for creative, nonlinear methods to strengthen blockchain security in light of present challenges including increasing attack vectors and risks posed by quantum computing. The suggested Dynamic Chaos-based Blockchain Security (DC-BS) system in this paper makes use of the chaotic dynamics present in ND to strengthen various aspects of blockchain security. Adaptive threat detection systems, dynamic consensus methods, and chaos-based encryption are all newly introduced in DC-BS. Validation of DC-BS's efficacy in preventing various attack scenarios through simulation studies demonstrates its advantages in reducing vulnerabilities and responding to new attack types. Various decentralized systems can benefit from DC-BS, including as supply chain management, the Internet of Things (IoT), conventional blockchain networks, and decentralized finance (DeFi). To strengthen the security of various decentralized applications, DC-BS works to increase trust, transparency, and resilience. The effectiveness of DCBS is confirmed by thorough simulation analyses that cover a wide range of attack scenarios, including double-spending assaults, Sybil attacks, and eclipse attacks. Based on the results of the simulations, DCBS is much more effective than conventional blockchain security procedures at reducing these risks. Showcased as well is the technique's capacity to react to changing attack techniques, highlighting its capacity to provide strong security even in dynamic settings.
Over the last fifteen years, the development of blockchain technologies has attracted a large volume of professional expertise, capital investment and media attention. This burgeoning sector of technology practices has coalesced around a few major initiatives (Bitcoin, Ethereum), but it is still moving at a fast pace and its configuration is evolving. If this sector is marked by a variety of technological protocols, financial arrangements and organizational forms, it is also, we would argue, a site of social effervescence. Parties, meet-ups, and the sorts of informal socializing which gather around events and networks of all kinds function to endow the blockchain sector with the characteristics of what, in cultural analysis, are often called “scenes”. The aim of this special issue is to examine the interest of the notion of scene for the analysis of blockchain practices. We argue that the notion of scene may be mobilized as a useful analytical framework not only for the study of blockchain practices, but for that of technology practices more generally. In this introductory article, we ask the following questions: how can the notion of scene contribute to the understanding of blockchain practices? And what sort of research agenda does the notion point to? In the following sections we first identify some “scenic” components in blockchain phenomena. Then we review how media discourses and academic scholarship have framed these phenomena to show that the scene perspective is undertheorized in the context of technology-related social groupings. Finally, we propose a framework to analyse the main dimensions of blockchain scenes, before presenting the contributions to the special issue. With this special issue, we aim to establish a research agenda around technology scenes at the junction of STS and cultural analysis.
The current online social network landscape is characterized by competition to get larger audiences leading to massive user migrations which will determine the shape of the future Web. However, user migration phenomena have not been fully understood and their driving mechanisms are still not well identified; in particular, the behaviors of hubs and the influence they exert on their followers are unclear. In this work, we focus on these aspects by analyzing the propensity of hubs to migrate towards a new social platform as a consequence of a shocking event; and the influence they exert on the decision of their neighbors of migrating to a new platform or staying on the native one. We conducted analysis on data made available after a user migration consequence of a hard fork involving two Web3 online social networks based on the blockchains Steem and Hive. Due to the blockchain nature of these Web3 platforms, we got detailed data about social and financial interactions among the users, along with information that allowed a precise reconstruction of the context surrounding the migration. The main findings suggest that different types of hubs apply different strategies when choosing to migrate, e.g. financial hubs diversify their strategy by staying and migrating at the same time. As for hub influence, results suggest that users directly interacting with hubs tend to migrate. In general, findings on influence indicate that understanding the activity and the influence of hubs is crucial in monitoring and controlling the user migration process.
Jakob Svennevik Notland, Mariusz Nowostawski, Jingyue Li
Blockchain systems run consensus rules as code to agree on the state of the distributed ledger and secure the network. Changing these rules can be risky and challenging. In addition, it can often be controversial and take much effort to make all the necessary participants agree to adopt a change. Arguably, Bitcoin has seen centralisation tendencies in pools and in development. However, how these tendencies influence blockchain governance has received minimal community and academic attention. Our study analyses the governmental structures in a blockchain by looking into the history of Bitcoin. We investigate the process of changing consensus rules through a grounded theory analysis comprising quantitative and qualitative data from 34 consensus forks in Bitcoin and Bitcoin Cash. The results reveal the decentralised behaviour in Bitcoin and blockchain. Our results are in contrast to related work, emphasising centralisation among miners and developers. Furthermore, our results show how the consensus-driven deployment techniques and governance of consensus rules are intertwined.
Decentralization is widely recognized as a crucial characteristic of blockchains that enables them to resist malicious attacks such as the 51% attack and the takeover attack. Prior research has primarily examined decentralization in blockchains employing the same consensus protocol or at the level of block producers. This paper presents the first individual-level measurement study comparing the decentralization of blockchains employing different consensus protocols. To facilitate cross-consensus evaluation, we present a two-level comparison framework and a new metric. We apply the proposed methods to Ethereum and Steem, two representative blockchains for which decentralization has garnered considerable interest. Our findings dive deeper into the level of decentralization, suggest the existence of centralization risk at the individual level in Steem, and provide novel insights into the cross-consensus comparison of decentralization in blockchains.
Wenrui Zuo, Aravindh Raman, Raúl J. Mondragón, Gareth Tyson
There has been growing interest in the so-called “Web3” movement. This loosely refers to a mix of decentralized technologies, often underpinned by blockchain technologies. Among these, Web3 social media platforms have begun to emerge. These store all social interaction data (e.g., posts) on a public ledger, removing the need for centralized data ownership and management. But this comes at a cost, which some argue is prohibitively expensive. As an exemplar within this growing ecosytem, we explore memo.cash, a microblogging service built on the Bitcoin Cash (BCH) blockchain. We gather data for 24K users, 317K posts, 2.57M user actions, which have facilitated $6.75M worth of transactions. A particularly unique feature is that users must pay BCH tokens for each interaction (e.g., posting, following). We study how this may impact the social makeup of the platform. We therefore study memo.cash as both a social network and a transaction platform.
Haoran Zhu, Xiaolin Chang, Jelena Mišić, Vojislav B. Mišić · 6 authors
Bitcoin is the largest Proof-of-Work (PoW) public blockchain but is vulnerable to various attacks like stubborn mining attack, which greatly downgrades both system throughput and benefits malicious miners (attackers). The existing works assume miners receive new blocks immediately after block generation, which is away from reality. This article aims to quantify the stubborn mining attack severity in an imperfect Bitcoin network in which there exists block receiving delay. In this article, we first develop an analytic model to capture blockchain dynamics, and then derive formulas of both relative revenue and system throughput, which are applied to study attack severity. Experiment results validate our quantitative analysis method and show that imperfect networks favor attackers. Moreover, the results recommend a blockchain system to be composed of small mining pools to get fair revenue distribution, and minimize its network delay and fork probability to get high TPS.
A cryptocurrency is a digital asset maintained by a decentralised system using cryptography. Investors in this emerging digital market are exploring the profitability potential of portfolios in place of single coins. Portfolios are particularly useful given that price forecasting in such a volatile market is challenging. The crypto market is a self-organised complex system where the complex inter-dependencies between the cryptocurrencies may be exploited to understand the market dynamics and build efficient portfolios. In this letter, we use network methods to identify highly decorrelated cryptocurrencies to create diversified portfolios using the Markowitz Portfolio Theory agnostic to future market behaviour. The performance of our network-based portfolios is optimal with 46 coins and superior to benchmarks up to an investment horizon of 14 days, reaching up to 1,066% average expected return within 1 day, with reasonable associated risks. We also show that popular cryptocurrencies are typically not included in the optimal portfolios. Past price correlations reduce risk and may improve the performance of crypto portfolios in comparison to methodologies based exclusively on price auto-correlations. Short-term crypto investments may be competitive to traditional high-risk investments such as the stock market or commodity market but call for caution given the high variability of prices.
Increasing public concerns about the environment have led to many studies that have explored current issues and approaches towards its protection. Much less studied, however, is topic of public opinion surrounding the impact that cryptocurrencies are having on the environment. The cryptocurrency market, in particular, bitcoin, currently rivals other top well-known assets such as precious metals and exchanged traded funds in market value, and its growing. This work examines public opinion expressed about the environmental impacts of bitcoin derived from Twitter feeds. Three primary research questions were addressed in this work related to topics of public interest, their location, and people and places involved. Our findings show that factions of of the public are interest in protecting the environment, with topics that resonate mainly related to energy. This discourse was also taking place at few similar locations with a mix of different people and places of interest.
Rasoul Amirzadeh, Asef Nazari, Dhananjay Thiruvady, Mong Shan Ee
This study identifies the key factors influencing the price movements of major cryptocurrencies, Bitcoin, Binance Coin, Ethereum, Litecoin, Ripple, and Tether, using Bayesian networks (BNs). This study addresses two key challenges: modelling price movements in highly volatile cryptocurrency markets and enhancing predictive performance through discretisation-aware Bayesian Networks. It analyses both macro-financial indicators (gold, oil, MSCI, S and P 500, USDX) and social media signals (tweet volume) as potential price drivers. Moreover, since discretisation is a critical step in the effectiveness of BNs, we implement a structured procedure to build 54 BNs models by combining three discretisation methods (equal interval, equal quantile, and k-means) with several bin counts. These models are evaluated using four metrics, including balanced accuracy, F1 score, area under the ROC curve and a composite score. Results show that equal interval with two bins consistently yields the best predictive performance. We also provide deeper insights into each network's structure through inference, sensitivity, and influence strength analyses. These analyses reveal distinct price-driving patterns for each cryptocurrency, underscore the importance of coin-specific analysis, and demonstrate the value of BNs for interpretable causal modelling in volatile cryptocurrency markets.
Blockchains are significantly easing trade finance, with billions of dollars worth of assets being transacted daily. However, analyzing these networks remains challenging due to the sheer volume and complexity of the data. We introduce a method named InnerCore that detects market manipulators within blockchain-based networks and offers a sentiment indicator for these networks. This is achieved through data depth-based core decomposition and centered motif discovery, ensuring scalability. InnerCore is a computationally efficient, unsupervised approach suitable for analyzing large temporal graphs. We demonstrate its effectiveness by analyzing and detecting three recent real-world incidents from our datasets: the catastrophic collapse of LunaTerra, the Proof-of-Stake switch of Ethereum, and the temporary peg loss of USDC - while also verifying our results against external ground truth. Our experiments show that InnerCore can match the qualified analysis accurately without human involvement, automating blockchain analysis in a scalable manner, while being more effective and efficient than baselines and state-of-the-art attributed change detection approach in dynamic graphs.
Recent years have witnessed the availability of richer and richer datasets in a variety of domains, where signals often have a multi-modal nature, blending temporal, relational and semantic information. Within this context, several works have shown that standard network models are sometimes not sufficient to properly capture the complexity of real-world interacting systems. For this reason, different attempts have been made to enrich the network language, leading to the emerging field of higher-order networks. In this work, we investigate the possibility of applying methods from higher-order networks to extract information from the online trade of Non-fungible tokens (NFTs), leveraging on their intrinsic temporal and non-Markovian nature. While NFTs as a technology open up the realms for many exciting applications, its future is marred by challenges of proof of ownership, scams, wash trading and possible money laundering. We demonstrate that by investigating time-respecting non-Markovian paths exhibited by NFT trades, we provide a practical path-based approach to fraud detection.
Aos Mulahuwaish, Matthew Loucks, Basheer Qolomany, Ala Al‐Fuqaha
Digital cryptocurrencies such as Bitcoin have exploded in recent years in both popularity and value. By their novelty, cryptocurrencies tend to be both volatile and highly speculative. The capricious nature of these coins is helped facilitated by social media networks such as Twitter. However, not everyone's opinion matters equally, with most posts garnering little to no attention. Additionally, the majority of tweets are retweeted from popular posts. We must determine whose opinion matters and the difference between influential and non-influential users. This study separates these two groups and analyzes the differences between them. It uses Hypertext-induced Topic Selection (HITS) algorithm, which segregates the dataset based on influence. Topic modeling is then employed to uncover differences in each group's speech types and what group may best represent the entire community. We found differences in language and interest between these two groups regarding Bitcoin and that the opinion leaders of Twitter are not aligned with the majority of users. There were 2559 opinion leaders (0.72% of users) who accounted for 80% of the authority and the majority (99.28%) users for the remaining 20% out of a total of 355,139 users.
Inferring the heterogeneous connection pattern of a networked system of multivariate time series observations is a key issue. In finance, the topological structure of financial connectedness in a network of assets can be a central tool for risk measurement. Against this, we propose a topological framework for variance decomposition analysis of multivariate time series in time and frequency domains. We build on the network representation of time–frequency generalized forecast error variance decomposition (GFEVD), and design a method to partition its maximal spanning tree into two components: (a) superhighways, i.e. the infinite incipient percolation cluster, for which nodes with high centrality dominate; (b) roads, for which low centrality nodes dominate. We apply our method to study the topology of shock transmission networks across cryptocurrency, carbon emission and energy prices. Results show that the topologies of short and long run shock transmission networks are starkly different, and that superhighways and roads considerably vary over time. We further document increased spillovers across the markets in the aftermath of the COVID-19 outbreak, as well as the absence of strong direct linkages between cryptocurrency and carbon markets.
In Bitcoin blockchain, miner nodes are likely to choose transactions with high fee to be included in a block. This makes transactions with high fee being processed fast, affecting the amount of transaction fee that users want to pay. The reward for a winning miner consists of transaction fee and newly issued coins, and hence the amount of newly issued coins also affects the miner decision to participate in the mining competition. In addition, mining reward also affects the total hash computing power, which plays an important role of Bitcoin security for reducing the success probability of security attack by a malicious miner. In this paper, we develop a mathematical model for analyzing the interaction between miner decision making and user actions in terms of transaction fees, transaction-confirmation time, and security. We analyze the transaction-inclusion process with queueing theory, while decision making processes of miners and users are analyzed in the context of Nash equilibrium. The numerical examples show how the mining costs and newly issued coins affect miner decision making.
Recent years have witnessed the dramatic popularity of cryptocurrencies, in which millions invest to join the cryptocurrency community or make financial gains [1]. Investors employ many ways to analyze a cryptocurrency, from a purely technical approach to a more utility-centred approach [2]. However, few technologies exist to help investors find cryptocurrencies with bright prospects through social metrics, an equally if not more important viewpoint to consider due to the importance of communities in the space. This paper proposes an application to evaluate cryptocurrencies based on social metrics by establishing scores and models with machine learning and other tools [3]. We verified the need for our application through surveys, applied it to test investment strategies, andconducted a qualitative evaluation of the approach. The results show that our tool benefits investors by providing them with a different lens to view cryptocurrencies and helps them make more thorough decisions.
Bishenghui Tao, Hong‐Ning Dai, Haoran Xie, Fu Lee Wang
The metaverse and its underlying blockchain technology have attracted extensive attention in the past few years. How to mine, process, and analyze the tremendous data generated by the metaverse systems has posed a number of challenges. Aiming to address them, we mainly focus 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 article, we analyze three metaverse-related systems: non-fungible token (NFT), Ethereum (ETH), and Bitcoin (BTC) from the structural-identity perspective. First, we conduct the complex network analysis of the metaverse network and obtain several new insights (i.e., power-law degree distribution, disconnection, disassortativity, preferential attachment, and non-rich-club effect). Secondly, based on such findings, we propose a novel representation learning method named structure-to-vector with random pace (SVRP) for learning both the latent representation and structural identity of the network. Thirdly, we conduct node classification and link prediction tasks with the integration of graph neural networks (GNNs). Empirical results on three real-world datasets demonstrate that our proposed SVRP outperforms other existing methods in multiple tasks. In particular, our SVRP achieves the highest node classification accuracy (Acc) (99.3$\%$) and$F$1-score (96.7$\%$) while only requiring original non-attributed graphs.
Shiv Sondhi, Sherif Saad, Kevin Shi, Mohammad Abdullah Al Mamun · 5 authors
Blockchain and distributed ledger technologies rely on distributed consensus algorithms. In recent years many consensus algorithms and protocols have been proposed; most of them are for permissioned blockchain networks. However, the performance of these algorithms is not well understood. This paper introduces an approach to evaluating consensus algorithms and blockchain platforms in a hostile network environment with the presence of byzantine and other network failures. The approach starts by using stochastic modeling to model the behaviors of consensus algorithms under different typical and faulty operational scenarios. Next, we implemented a blockchain application using different consensus protocols and tested their performance using chaos engineering techniques. To demonstrate our generic evaluation approach, we analyze the performance of four permissioned blockchain platforms and their consensus protocols. Our results showed that stochastic modeling is an inexpensive and efficient technique for analyzing consensus protocols. But they do not represent the actual performance of the consensus protocols in a production environment. Moreover, an experiment with chaos engineering indicates that if two different blockchain platforms use the same blockchain algorithm or protocol, we should not assume they will have similar performance. Therefore, It is also essential to consider the role of platform architecture and how the protocols are engineered in a given platform.
The network formation process in the Bitcoin protocol is designed to hide the global network structure: while most of the nodes of the network can be easily discovered, the existence of an edge between two nodes is only known by the two endpoints. In [Becchetti et al., SODA2020] the authors propose a dynamic random graph model inspired by the network formation process in the Bitcoin protocol and they prove that the evolution of the graph quickly terminates and that the resulting graph is an expander, with high probability.