The Experts below are selected from a list of 5538 Experts worldwide ranked by ideXlab platform

Qi Yuan - One of the best experts on this subject based on the ideXlab platform.

  • t edge temporal weighted multidigraph embedding for Ethereum transaction network analysis
    Frontiers in Physics, 2020
    Co-Authors: Dan Lin, Qi Yuan, Zibin Zheng
    Abstract:

    Recently, graph embedding techniques have been widely used in the analysis of various networks, but most of the existing embedding methods omit the network dynamics and the multiplicity of edges, so it is difficult to accurately describe the detailed characteristics of the transaction networks. Ethereum is a blockchain-based platform supporting smart contracts. The open nature of blockchain makes the transaction data on Ethereum completely public, and also brings unprecedented opportunities for the transaction network analysis. By taking the realistic rules and features of transaction networks into consideration, we first model the Ethereum transaction network as a Temporal Weighted Multidigraph (TWMDG), where each node is a unique Ethereum account and each edge represents a transaction weighted by amount and assigned with timestamp. Then we define the problem of Temporal Weighted Multidigraph Embedding (T-EDGE) by incorporating both temporal and weighted information of the edges, the purpose being to capture more comprehensive properties of dynamic transaction networks. To evaluate the effectiveness of the proposed embedding method, we conduct experiments of node classification on real-world transaction data collected from Ethereum. Experimental results demonstrate that T-EDGE outperforms baseline embedding methods, indicating that time-dependent walks and multiplicity characteristic of edges are informative and essential for time-sensitive transaction networks.

  • Modeling and Understanding Ethereum Transaction Records via a Complex Network Approach
    IEEE Transactions on Circuits and Systems II: Express Briefs, 2020
    Co-Authors: Dan Lin, Qi Yuan, Zibin Zheng
    Abstract:

    As the largest public blockchain-based platform supporting smart contracts, Ethereum has accumulated a large number of user transaction records since its debut in 2014. Analysis of Ethereum transaction records, however, is still relatively unexplored till now. Modeling the transaction records as a static simple graph, existing methods are unable to accurately characterize the temporal and multiplex features of the edges. In this brief, we first model the Ethereum transaction records as a complex network by incorporating time and amount features of the transactions, and then design several flexible temporal walk strategies for random-walk based graph representation of this large-scale network. Experiments of temporal link prediction on real Ethereum data demonstrate that temporal information and multiplicity characteristic of edges are indispensable for accurate modeling and understanding of Ethereum transaction networks.

  • T-EDGE: Temporal WEighted MultiDiGraph Embedding for Ethereum Transaction Network Analysis
    'Frontiers Media SA', 2020
    Co-Authors: Wu Jiajing, Qi Yuan, Lin Dan, Zheng Zibin
    Abstract:

    Recently, graph embedding techniques have been widely used in the analysis of various networks, but most of the existing embedding methods omit the network dynamics and the multiplicity of edges, so it is difficult to accurately describe the detailed characteristics of the transaction networks. Ethereum is a blockchain-based platform supporting smart contracts. The open nature of blockchain makes the transaction data on Ethereum completely public, and also brings unprecedented opportunities for the transaction network analysis. By taking the realistic rules and features of transaction networks into consideration, we first model the Ethereum transaction network as a Temporal Weighted Multidigraph (TWMDG), where each node is a unique Ethereum account and each edge represents a transaction weighted by amount and assigned with timestamp. Then we define the problem of Temporal Weighted Multidigraph Embedding (T-EDGE) by incorporating both temporal and weighted information of the edges, the purpose being to capture more comprehensive properties of dynamic transaction networks. To evaluate the effectiveness of the proposed embedding method, we conduct experiments of node classification on real-world transaction data collected from Ethereum. Experimental results demonstrate that T-EDGE outperforms baseline embedding methods, indicating that time-dependent walks and multiplicity characteristic of edges are informative and essential for time-sensitive transaction networks.Comment: 12 page

Dan Lin - One of the best experts on this subject based on the ideXlab platform.

  • t edge temporal weighted multidigraph embedding for Ethereum transaction network analysis
    Frontiers in Physics, 2020
    Co-Authors: Dan Lin, Qi Yuan, Zibin Zheng
    Abstract:

    Recently, graph embedding techniques have been widely used in the analysis of various networks, but most of the existing embedding methods omit the network dynamics and the multiplicity of edges, so it is difficult to accurately describe the detailed characteristics of the transaction networks. Ethereum is a blockchain-based platform supporting smart contracts. The open nature of blockchain makes the transaction data on Ethereum completely public, and also brings unprecedented opportunities for the transaction network analysis. By taking the realistic rules and features of transaction networks into consideration, we first model the Ethereum transaction network as a Temporal Weighted Multidigraph (TWMDG), where each node is a unique Ethereum account and each edge represents a transaction weighted by amount and assigned with timestamp. Then we define the problem of Temporal Weighted Multidigraph Embedding (T-EDGE) by incorporating both temporal and weighted information of the edges, the purpose being to capture more comprehensive properties of dynamic transaction networks. To evaluate the effectiveness of the proposed embedding method, we conduct experiments of node classification on real-world transaction data collected from Ethereum. Experimental results demonstrate that T-EDGE outperforms baseline embedding methods, indicating that time-dependent walks and multiplicity characteristic of edges are informative and essential for time-sensitive transaction networks.

  • Modeling and Understanding Ethereum Transaction Records via a Complex Network Approach
    IEEE Transactions on Circuits and Systems II: Express Briefs, 2020
    Co-Authors: Dan Lin, Qi Yuan, Zibin Zheng
    Abstract:

    As the largest public blockchain-based platform supporting smart contracts, Ethereum has accumulated a large number of user transaction records since its debut in 2014. Analysis of Ethereum transaction records, however, is still relatively unexplored till now. Modeling the transaction records as a static simple graph, existing methods are unable to accurately characterize the temporal and multiplex features of the edges. In this brief, we first model the Ethereum transaction records as a complex network by incorporating time and amount features of the transactions, and then design several flexible temporal walk strategies for random-walk based graph representation of this large-scale network. Experiments of temporal link prediction on real Ethereum data demonstrate that temporal information and multiplicity characteristic of edges are indispensable for accurate modeling and understanding of Ethereum transaction networks.

Zibin Zheng - One of the best experts on this subject based on the ideXlab platform.

  • t edge temporal weighted multidigraph embedding for Ethereum transaction network analysis
    Frontiers in Physics, 2020
    Co-Authors: Dan Lin, Qi Yuan, Zibin Zheng
    Abstract:

    Recently, graph embedding techniques have been widely used in the analysis of various networks, but most of the existing embedding methods omit the network dynamics and the multiplicity of edges, so it is difficult to accurately describe the detailed characteristics of the transaction networks. Ethereum is a blockchain-based platform supporting smart contracts. The open nature of blockchain makes the transaction data on Ethereum completely public, and also brings unprecedented opportunities for the transaction network analysis. By taking the realistic rules and features of transaction networks into consideration, we first model the Ethereum transaction network as a Temporal Weighted Multidigraph (TWMDG), where each node is a unique Ethereum account and each edge represents a transaction weighted by amount and assigned with timestamp. Then we define the problem of Temporal Weighted Multidigraph Embedding (T-EDGE) by incorporating both temporal and weighted information of the edges, the purpose being to capture more comprehensive properties of dynamic transaction networks. To evaluate the effectiveness of the proposed embedding method, we conduct experiments of node classification on real-world transaction data collected from Ethereum. Experimental results demonstrate that T-EDGE outperforms baseline embedding methods, indicating that time-dependent walks and multiplicity characteristic of edges are informative and essential for time-sensitive transaction networks.

  • Modeling and Understanding Ethereum Transaction Records via a Complex Network Approach
    IEEE Transactions on Circuits and Systems II: Express Briefs, 2020
    Co-Authors: Dan Lin, Qi Yuan, Zibin Zheng
    Abstract:

    As the largest public blockchain-based platform supporting smart contracts, Ethereum has accumulated a large number of user transaction records since its debut in 2014. Analysis of Ethereum transaction records, however, is still relatively unexplored till now. Modeling the transaction records as a static simple graph, existing methods are unable to accurately characterize the temporal and multiplex features of the edges. In this brief, we first model the Ethereum transaction records as a complex network by incorporating time and amount features of the transactions, and then design several flexible temporal walk strategies for random-walk based graph representation of this large-scale network. Experiments of temporal link prediction on real Ethereum data demonstrate that temporal information and multiplicity characteristic of edges are indispensable for accurate modeling and understanding of Ethereum transaction networks.

Ting Chen - One of the best experts on this subject based on the ideXlab platform.

  • Understanding Ethereum via Graph Analysis
    ACM Transactions on Internet Technology, 2020
    Co-Authors: Ting Chen, Yuxiao Zhu, Xiapu Luo, Jiachi Chen, Xiaodong Lin, John C. S. Lui, Xiaosong Zhang
    Abstract:

    Ethereum, a blockchain, supports its own cryptocurrency named Ether and smart contracts. Although more than 8M smart contracts have been deployed on Ethereum, little is known about the characterist...

  • Understanding Ethereum via Graph Analysis
    ACM Transactions on Internet Technology, 2020
    Co-Authors: Ting Chen, Yuxiao Zhu, Xiapu Luo, Jiachi Chen, Xiaodong Lin, John C. S. Lui, Xiaosong Zhang
    Abstract:

    Ethereum, a blockchain, supports its own cryptocurrency named Ether and smart contracts. Although more than 8M smart contracts have been deployed on Ethereum, little is known about the characteristics of its users, smart contracts, and the relationships among them. We conduct the first systematic study on Ethereum by leveraging graph analysis to characterize three major activities on Ethereum, namely money transfer, smart contract creation, and smart contract invocation. We collect all transaction data, construct three graphs from the data to characterize major activities via graph analysis, and discover new insights. Moreover, we address three security issues based on graphs.

  • ICDCS - DataEther: Data Exploration Framework For Ethereum
    2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), 2019
    Co-Authors: Ting Chen, Xiapu Luo, Jiachi Chen, Xiaosong Zhang, Yufei Zhang, Ang Chen, Kun Yang
    Abstract:

    Ethereum is the largest blockchain platform supporting smart contracts with the second biggest market capitalization. Ethereum data can yield many useful insights because of the large volume of transactions, accounts and blocks as well as the popular applications developed as smart contracts. Studying Ethereum data can also reveal many new attacks to the platform and its smart contracts. Unfortunately, it is non-trivial to systematically explore Ethereum because it involves massive heterogeneous data, which are produced and stored in different ways. Although a few recent studies report some interesting observations about Ethereum, they are limited by their data acquisition methods which cannot provide comprehensive and precise data. In this paper, to fill the gap, we propose DataEther, a systematic and high-fidelity data exploration framework for Ethereum by exploiting its internal mechanisms. Besides supporting the analyses in existing studies, DataEther further empowers users to explore unknown phenomena and obtain in-depth understandings. We first describe how we tackle the challenging issues in developing DataEther, and then use four data-centric applications to demonstrate its usage and report many new observations.

  • Understanding Ethereum Via Graph Analysis
    Infocom, 2018
    Co-Authors: Ting Chen, Yuxiao Zhu, Xiapu Luo, Jiachi Chen, Xiaoqi Li, Zihao Li, Xiaodong Lin, Xiaosong Zhang
    Abstract:

    — Being the largest blockchain with the capability of running smart contracts, Ethereum has attracted wide atten-tion and its market capitalization has reached 20 billion USD. Ethereum not only supports its cryptocurrency named Ether but also provides a decentralized platform to execute smart contracts in the Ethereum virtual machine. Although Ether's price is approaching 200 USD and nearly 600K smart contracts have been deployed to Ethereum, little is known about the characteristics of its users, smart contracts, and the relationships among them. To fill in the gap, in this paper, we conduct the first systematic study on Ethereum by leveraging graph analysis to characterize three major activities on Ethereum, namely money transfer, smart contract creation, and smart contract invocation. We design a new approach to collect all transaction data, construct three graphs from the data to characterize major activities, and discover new observations and insights from these graphs. Moreover, we propose new approaches based on cross-graph analysis to address two security issues in Ethereum. The evaluation through real cases demonstrates the effectiveness of our new approaches.

  • INFOCOM - Understanding Ethereum via Graph Analysis
    IEEE INFOCOM 2018 - IEEE Conference on Computer Communications, 2018
    Co-Authors: Ting Chen, Yuxiao Zhu, Xiapu Luo, Jiachi Chen, Xiaodong Lin, Xiaosong Zhange
    Abstract:

    Being the largest blockchain with the capability of running smart contracts, Ethereum has attracted wide attention and its market capitalization has reached 20 billion USD. Ethereum not only supports its cryptocurrency named Ether but also provides a decentralized platform to execute smart contracts in the Ethereum virtual machine. Although Ether's price is approaching 200 USD and nearly 600K smart contracts have been deployed to Ethereum, little is known about the characteristics of its users, smart contracts, and the relationships among them. To fill in the gap, in this paper, we conduct the first systematic study on Ethereum by leveraging graph analysis to characterize three major activities on Ethereum, namely money transfer, smart contract creation, and smart contract invocation. We design a new approach to collect all transaction data, construct three graphs from the data to characterize major activities, and discover new observations and insights from these graphs. Moreover, we propose new approaches based on cross-graph analysis to address two security issues in Ethereum. The evaluation through real cases demonstrates the effectiveness of our new approaches.

Jianyu Niu - One of the best experts on this subject based on the ideXlab platform.

  • Selfish Mining in Ethereum
    arXiv: Cryptography and Security, 2019
    Co-Authors: Chen Feng, Jianyu Niu
    Abstract:

    As the second largest cryptocurrency by market capitalization and today's biggest decentralized platform that runs smart contracts, Ethereum has received much attention from both industry and academia. Nevertheless, there exist very few studies about the security of its mining strategies, especially from the selfish mining perspective. In this paper, we aim to fill this research gap by analyzing selfish mining in Ethereum and understanding its potential threat. First, we introduce a 2-dimensional Markov process to model the behavior of a selfish mining strategy inspired by a Bitcoin mining strategy proposed by Eyal and Sirer. Second, we derive the stationary distribution of our Markov model and compute long-term average mining rewards. This allows us to determine the threshold of computational power that makes selfish mining profitable in Ethereum. We find that this threshold is lower than that in Bitcoin mining (which is 25% as discovered by Eyal and Sirer), suggesting that Ethereum is more vulnerable to selfish mining than Bitcoin.

  • ICDCS - Selfish Mining in Ethereum
    2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), 2019
    Co-Authors: Chen Feng, Jianyu Niu
    Abstract:

    As the second largest cryptocurrency by market capitalization and today's biggest decentralized platform that runs smart contracts, Ethereum has received much attention from both academia and industry. Nevertheless, there exist very few studies about the security of its mining strategies, especially from the selfish mining perspective. In this paper, we fill this research gap by analyzing selfish mining in Ethereum and understanding its potential threat. First, we introduce a 2-dimensional Markov process to model the behavior of a selfish mining strategy inspired by a Bitcoin mining strategy proposed by Eyal and Sirer. Second, we derive the stationary distribution of our Markov model and compute long-term average mining rewards. This allows us to determine the threshold of computational power which makes selfish mining profitable in Ethereum. We find that this threshold is lower than that in Bitcoin mining (which is 25% as discovered by Eyal and Sirer), suggesting that Ethereum is more vulnerable to selfish mining than Bitcoin.