Economic Analysis of Decentralized Exchange Market with Transaction Fee Mining

Fan, Sizheng; Fang, Zhixuan; Cai, Wei

doi:10.1145/3494106.3528677

Cited by 7 publications

(2 citation statements)

References 11 publications

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“…In the line of studies on modeling BCNs, a substantial part of them focuses on price prediction. Literatures [11], [32], [33], [34], [35], and [36] provide various models for the analysis of the cryptocurrency prices or transaction fees, such as Gradient Boosting Decision Tree, neural networks, and long short-term memory (LSTM) networks. On the other hand, other studies focus on characterizing graphs and detecting cryptocurrency BCNs.…”

Section: B Modeling Bcnsmentioning

confidence: 99%

Structural Identity Representation Learning for Blockchain-Enabled Metaverse Based on Complex Network Analysis

Tao

Dai

Xie³

et al. 2023

IEEE Trans. Comput. Soc. Syst.

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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 realworld 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 F1-score (96.7%) while only requiring original nonattributed graphs.

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Section: B Modeling Bcnsmentioning

confidence: 99%

Structural Identity Representation Learning for Blockchain-Enabled Metaverse Based on Complex Network Analysis

Tao

Dai

Xie³

et al. 2023

IEEE Trans. Comput. Soc. Syst.

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“…The boom of specific blockchain projects has dramatically contributed to the growth of blockchain games. For example, decentralized exchanges (DEXs) [24] can meet players' demand to exchange tokens; decentralized autonomous organizations (DAOs) can lower the barrier to participating in blockchain games by providing players with training sessions and lending services of ingame non-fungible tokens (NFTs) [11]. These factors have led to the rise of blockchain games in 2020.…”

Section: Introductionmentioning

confidence: 99%

Towards Understanding Player Behavior in Blockchain Games: A Case Study of Aavegotchi

Jiang

Min

Fan

et al. 2022

Proceedings of the 17th International Conference on the Foundations of Digital Games

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Blockchain games introduce unique gameplay and incentive mechanisms by allowing players to be rewarded with in-game assets or tokens through financial activities. However, most blockchain games are not comparable to traditional games in terms of lifespan and player engagement. In this paper, we try to see the big picture in a small way to explore and determine the impact of gameplay and financial factors on player behavior in blockchain games. Taking Aavegotchi as an example, we collect one year of operation data to build player profiles. We perform an in-depth analysis of player behavior from the macroscopic data and apply an unsupervised clustering method to distinguish the attraction of the gameplay and incentives. Our results reveal that the whole game is held up by a small number of players with high-frequent interaction or vast amounts of funds invested. Financial incentives are indispensable for blockchain games for they provide attraction and optional ways for players to engage with the game. However, financial services are tightly linked to the free market. The game will face an irreversible loss of players when the market experiences depression. For blockchain games, well-designed gameplay should be the fundamental basis for the long-lasting retention of players. CCS CONCEPTS• Human-centered computing → Empirical studies in collaborative and social computing; • Applied computing → Computer games.

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Towards understanding governance tokens in liquidity mining: a case study of decentralized exchanges

Fan

Min

Wu³

et al. 2022

World Wide Web

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Economic Analysis of Decentralized Exchange Market with Transaction Fee Mining

Cited by 7 publications

References 11 publications

Structural Identity Representation Learning for Blockchain-Enabled Metaverse Based on Complex Network Analysis

Structural Identity Representation Learning for Blockchain-Enabled Metaverse Based on Complex Network Analysis

Towards Understanding Player Behavior in Blockchain Games: A Case Study of Aavegotchi

Towards understanding governance tokens in liquidity mining: a case study of decentralized exchanges

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