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  1. NTU Theses and Dissertations Repository
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請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/86342
標題: NFT社會網絡分析初探:以CryptoPunks為例
A Preliminary Exploration of NFT Social Network Analysis: CryptoPunks
作者: Ching-Chun Cheng
鄭敬錞
指導教授: 吳政鴻(Cheng-Hung Wu)
關鍵字: NFT,CryptoPunks,社會網絡分析,巨鯨,
NFT,CryptoPunks,Social Network Analysis,Whales,
出版年 : 2022
學位: 碩士
摘要: 非同質性代幣NFT(Non-Fungible Token)自國際知名拍賣行「佳士得」2021年拍賣出史上最高金額6900萬美金的數位藝術品NFT《EVERYDAYS: THE FIRST 5000 DAYS》(每一天:最初的五千天),改變了美國數位藝術創作者Beeple的一生,讓他榮登全球拍賣成交金額第三高位的在世藝術家(Living artists),以及改變藝術拍賣市場的商業模式,創造全新的市場商機。 本文透過社會網絡分析法與個案研究,找出扮演在NFT藍籌項目中關鍵影響力的交易者、平台或智能合約的特徵,透過了解這些有影響力的交易者,刻劃出NFT藍籌社群影響者的樣態,與其獲利表現進行比較。並進一步探討巨鯨(Whale)在NFT的「交易」網絡關係,探索其獲利是否有顯著的正向表現,過去的NFT研究多著重在資產定價,較缺乏對整體社群與影響者特徵的分析。 本文發現社會網絡分析法主要指標可快速找出較受歡迎的服務與影響者,其方法中的模組度分類分群有可靠度,可有效的辨認未知的位址是與哪個社群相關,此外也發現在鏈上影響力大不一定代表在實際的社群(如:Twitter)影響力也大。 而在 CryptoPunks影響者與巨鯨的獲利表現中發現以下幾點:一、發現點度中心性排名前十大節點獲利高,可能為潛在的獲利指標;二、具有影響力的節點仍有可能為非獲利的兩種情況:短暫持有錢包或為智能合約;三、巨鯨數量相對與整體網絡佔比為7.08%,但交易量佔比卻超過27.6%,具顯著的價格操作可能;四、前十大點度中心性排行的巨鯨,總獲利的表現非常驚人,獲利最高可達2993萬美金,最低73萬美金,而且中介中心性與特徵向量中心性也在前3% 的排名內。
NFT (Non-Fungible Token) will auction the digital artwork NFT 'EVERYDAYS: THE FIRST 5000 DAYS', changed the whole life of Beeple, an American digital art creator, made him the third-highest living artist in the world in terms of auction turnover, and which changed the business model of the art auction market, creating new market opportunities. Through social network analysis and case studies, this paper finds out the characteristics of traders, platforms or smart contracts that play a key role in NFT blue-chip projects. By understanding these influential traders, we can characterize the influence of the NFT blue-chip community and compare their profit performance. Moreover, we will further investigate the 'trading' network relationship of Whale in NFT and explore whether its profit has a significant positive performance. In the past, NFT research mostly focused on asset pricing, and lacked analysis of the characteristics of the overall community and influencers. This paper finds that the main indicators of the social network analysis method that can quickly find out the more popular services and influencers, and the modular classification and grouping in the method has a certain degree of reliability, which can effectively identify which community an unknown address is related to , and also found that a large influence on the chain does not necessarily mean a large influence in the actual community (such as Twitter). Based on the profit performance of CryptoPunks influencers and giant whales, we observed a few points: 1. It is found that the top ten nodes in the centrality of the point have high profits, which may be potential profit indicators; 2. The influential nodes are still possible. Two situations that are not profitable: holding wallets for a short time or smart contracts; 3. The number of giant whales accounts for 7.08% of the overall network, but the transaction volume accounts for more than 27.6%, which has a significant possibility of price manipulation; 4. The top ten giant whales ranked in the top ten point centrality, the total profit performance is still very amazing, the highest profit can reach up to 29.93 million US dollars, and the lowest can be 730,000 US dollars, and the intermediary centrality and eigenvector centrality are also in the top 3 percentage ranking.
URI: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/86342
DOI: 10.6342/NTU202202772
全文授權: 同意授權(全球公開)
電子全文公開日期: 2022-08-31
顯示於系所單位:工業工程學研究所

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