-

 

Temporal Analysis of the Entire Ethereum Blockchain Network

A summary of the Web Conference 2021 research paper by Lin Zhao, Sourav Sen Gupta, Arijit Khan, and Robby Luo

Background: With over USD 42 Billion market capitalization (October 2020), Ethereum is the largest public blockchain that supports smart contracts [1]. Ethereum is a transaction-based state-transition machine, where the state is made up of accounts. Transfer of asset and information between accounts, recorded in the blockchain, cause transitions in the Ethereum ‘world state’. There are two types of accounts in Ethereum – users and contracts. Transactions in Ethereum are initiated by user accounts, signed with their private keys, while internal messages in Ethereum can be generated by contract accounts. Ether is the primary asset (currency) for Ethereum blockchain. In addition to ether, Ethereum blockchain allows creation of Tokens, an abstraction of digital assets, via relevant methods implemented through smart contracts. Similar to transacting ether (the base currency), Ethereum accounts may also transact in various Tokens, through mechanisms defined in the respective smart contracts. This allows for a complex asset-transfer-ecosystem of various fungible (e.g., ERC20) and non-fungible (e.g., ERC721) tokens (assets) to flourish on Ethereum blockchain.

Ethereum, and similar public blockchains supporting smart contracts, also bring forth a fascinating ecosystem of humans (users) and autonomous agents (contracts), cohabiting the underlying blockchain fabric. It is neither like online social networks, where the players are all human users, nor like the core financial networks, where all interactions are transfer of value or asset. In essence, a blockchain network like Ethereum is closer to the Internet or Web, where users and programs are allowed to interact with one another, following predefined rules of engagement. In addition to this Web-like architecture, there is also an interaction framework for smart contracts (agents), where they can call, invoke, or kill each other to maintain and advance the ‘world state’ of the blockchain. This motivates us to study Ethereum blockchain, as a representative of similar public blockchain networks supporting decentralized automation through smart contracts. We are interested in all interactions in the ecosystem: user-to-user, user-to-contract, contract-to-user, and contract-to-contract [2].

                               Figure 1: Interactions in the Ethereum Blockchain Network

Related Work and Motivation. Recent works [3, 4, 5, 6, 2, 7, 8, 9] have modeled transactions, tokens, and other interactions in the Ethereum blockchain as static graphs to provide new observations and insights by conducting relevant graph analysis. Surprisingly, there is much less study on the evolution and temporal properties of these networks. In this paper, we investigate the evolutionary nature of Ethereum interaction networks from a temporal graph perspective. Specifically, we aim at addressing three main research questions:

(1) How do Ethereum blockchain networks evolve over time? What growth model do they follow? What is the active lifespan of each vertex? How do the high-degree vertices change over time?

(2) How network properties (e.g., reciprocity, assortativity, clustering coefficient, core decomposition) change over time for Ethereum blockchain networks? Do they indicate anomalies and other external aspects of the network (e.g., popularity, exchanges)? What is the right “time granularity” for such temporal analysis?

(3) Can we detect meaningful communities in Ethereum blockchain networks, and also forecast the ‘continuation’ (survival) of these communities in succeeding months leveraging on the relevant graph properties (features) and machine learning models?

Our Contributions: Our main contributions are as follows.

(1) To the best of our knowledge, we are the first to conduct a comprehensive study of the evolutionary, temporal, and predictive aspects of the large-scale Ethereum blockchain network, cohabited by both human users and autonomous smart contracts. We investigate their complex interactions by constructing four temporal networks from the entire Ethereum blockchain data, namely TraceNet, ContractNet, TransactionNet, and TokenNet, at various time granularities. We open source our code and dataset [10].

(2) We study the annual growth rate of four blockchain networks, demonstrating that Ethereum interaction networks are growing at a fast speed and the account information is updated at a fast pace, however all the graphs get sparser and mature over time, and follow the preferential attachment growth model. The user accounts remain active much longer than smart contracts on Ethereum.

(3) We employ global network properties such as reciprocity, assortativity, clustering coefficient, and core decomposition, to detect significant changes and anomalies over Ethereum blockchain networks. We correlate these anomalies with external aspects of the network, e.g., popularity, exchanges, and systematically drill-down to the appropriate time granularity for our analyses.

(4) We forecast the ‘continuation’ (survival) of certain network communities in succeeding months leveraging on the relevant graph properties (as features) and machine learning models, achieving up to 77% correct predictions for continuation.

Our results will be useful for emerging fields such as blockchain intelligence (https://blockchaingroup.io) and blockchain-based social networks [11, 12] that are building blockchain search engines, making use of data mining and analytics skills to help clients avoid transaction risks. We matched our network analysis results with real-world incidents. Researchers working in natural language processing and sentiment analysis using tweets and online articles about blockchain [13, 14] can find supporting views and references from our work. Our community longevity prediction method and results can be utilized by companies to build blockchain ecosystems.

For more information, click here for our paper. Code: GitHub.

References

[1] Coinmarketcap. 2020. About Ethereum. https://coinmarketcap.com/currencies/ ethereum/.

[2] X. T. Lee, A. Khan, S. S. Gupta, Y. H. Ong, and X. Liu. 2020. Measurements, Analyses, and Insights on the Entire Ethereum Blockchain Network. In WWW.

[3] C. G. Akcora, Y. R. Gel, and M. Kantarcioglu. 2017. Blockchain: A Graph Primer. CoRR abs/1708.08749 (2017).

[4]  T. Chen, Y. Zhu, Z. Li, J. Chen, X. Li, X. Luo, X. Lin, and X. Zhang. 2018. Understanding Ethereum via Graph Analysis. In INFOCOM. IEEE, 1484–1492.

[5] S. Ferretti and G. D’Angelo. 2019. On the Ethereum Blockchain Structure: A Complex Networks Theory Perspective. Concurrency and Computation: Practice and Experience (2019), e5493.

[6] L. Kiffer, D. Levin, and A. Mislove. 2018. Analyzing Ethereum’s Contract Topology. In Internet Measurement Conference.

[7] S. Somin, G. Gordon, and Y. Altshuler. 2018. Network Analysis of ERC20 Tokens Trading on Ethereum Blockchain. In Complex Systems.

[8] S. Somin, G. Gordon, and Y. Altshuler. 2018. Social Signals in the Ethereum Trading Network. CoRR abs/1805.12097 (2018). arXiv:1805.12097 http://arxiv. org/abs/1805.12097.

[9] F. Victor and B. K. Lüders. 2019. Measuring Ethereum-based ERC20 Token Networks. In Financial Cryptography and Data Security.

[10] L. Zhao, S. Sen Gupta, A. Khan, and R. Luo. 2021. Temporal Analysis of the Entire Ethereum Blockchain Network (Code and Dataset). https://github.com/ LinZhao89/Ethereum-analysis.

[11] GLOBE NEWSWIRE. 2020. CoinLinked Debuts as First-Ever Blockchain-Based Social Network and Crypto-Commerce Platform. https://www.globenewswire. com/news-release/2020/05/07/2029366/0/en/CoinLinked-Debuts-as-First-EverBlockchain-Based-Social-Network-and-Crypto-Commerce-Platform.html.

[12] F. E. Oggier, A. Datta, and S. Phetsouvanh. 2020. An Ego Network Analysis of Sextortionists. Soc. Netw. Anal. Min. 10, 1 (2020), 44

[13] O. Kraaijeveld and J. D. Smedt. 2020. The Predictive Power of Public Twitter Sentiment for Forecasting Cryptocurrency Prices. Journal of International Financial Markets, Institutions and Money 65, C (2020), S104244312030072X.

[14] A.-D. Vo, Q.-P. Nguyen, and C.-Y. Ock. 2019. Sentiment Analysis of News for Effective Cryptocurrency Price Prediction. International Journal of Knowledge Engineering 5, 2 (2019), 47–52.

Comments

Post a Comment

Popular posts from this blog

Measurements, Analyses, and Insights on the Entire Ethereum Blockchain Network

-
Image
Measurements, Analyses, and Insights on the Entire Ethereum Blockchain Network A summary of the WebConf 2020 (formerly WWW) research  paper  by Xi Tong Lee, Arijit Khan, Sourav Sen Gupta, Yu Hann Ong, and Xuan Liu. Background of the Ethereum Blockchain :  It has been more than ten years since Bitcoin [1] introduced the era of decentralized community-controlled currency. Since then, several cryptocurrency variants like Litecoin, Namecoin, Dash, Zcash, have been introduced. Blockchains are increasingly becoming popular due to the prevalence of such cryptocurrencies and decentralized applications. Decentralized applications are written on the framework of numerous blockchain networks like Hyperledger, Corda, Ripple, Stellar, EOS, NEO, IOTA, and many more. Among them, Ethereum [2] is a distributed public blockchain network that focuses on running code (smart contracts) for decentralized applications. More simply, it is a platform for sharing information in a global state that can
Read more
-
Image
  Neighborhood-based Hypergraph Core Decomposition A summary of the PVLDB 2023 research paper  by Naheed Anjum Arafat, Arijit Khan, Arpit Kumar Rai, and Bishwamittra Ghosh Background: Many real-world relations consist of polyadic entities, e.g., relations between individuals in co-authorships [1], legislators in parliamentary voting [2], items in e-shopping carts [3], proteins in protein complexes, and metabolites in a metabolic process [4, 5]. In these scenarios, the hypergraph is a natural data model where an edge may connect more than two entities. Recently, there has been a growing interest in hypergraph data management [6, 7, 8, 9, 10, 11]. We study neighborhood-based hypergraph core decomposition (Figure 1(a)): a novel way of decomposing hypergraphs into hierarchical neighborhood-cohesive subhypergraphs. It decomposes a hypergraph into nested, strongly-induced maximal subhypergraphs such that all the nodes in every subhypergraph have at least a certain number of neighbors i
Read more
-
Image
  Distributed Graph Embedding with Information-Oriented Random Walks A summary of the PVLDB 2023 research  paper  by Peng Fang, Arijit Khan, Siqiang Luo, Fang Wang, Dan Feng, Zhenli Li, Wei Yin, and Yuchao Cao Background [Graph Embedding]: Graph embedding maps graph nodes to low-dimensional vectors and is widely adopted in machine learning tasks such as link prediction [1], node classification [2], clustering [3], and recommendation [4]. Sequential graph embedding techniques [5] fall into three categories. (1) Matrix factorization-based algorithms [6, 7, 8, 9, 10] construct feature representations based on the adjacency or Laplacian matrix and involve spectral techniques [11]. (2) Graph neural networks (GNNs)-based approaches [12, 13, 14, 15, 16] focus on generalizing graph spectra into semi-supervised or supervised graph learning. Both techniques incur high computational overhead and DRAM dependencies, limiting their scalability to large graphs. (3) A plethora of random walk-base
Read more