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| Title: | 應用Personalized PageRank與RFM模式區隔比特幣投資者類群
Classifying the segmentation of Bitcoin investors via personalized PageRank and RFM model |
| Authors: | 林聖翔
Lin, Sheng-Hsiang |
| Contributors: | 楊建民
洪為璽
Yang, Chien-Min
Hung, Wei-Hsi
林聖翔
Lin, Sheng-Hsiang |
| Keywords: | 比特幣
投資者區隔
Personalized PageRank
RFM模式
K-Means
Bitcoin
Investor segmentation
Personalized PageRank
RFM model
K-Means |
| Date: | 2019 |
| Issue Date: | 2019-08-07 16:07:03 (UTC+8) |
| Abstract: | 比特幣是一種對等式架構(peer-to-peer, p2p)之去中心化的貨幣系統，為目前區塊鏈技術最為人所知的應用，近年來隨著網路及大眾媒體的傳播，比特幣受到投資人的青睞，因此交易活動逐年劇增，匯率也水漲船高，而累積的紀錄構成了龐大的交易網絡，因此本研究欲透過比特幣區塊鏈的交易資料來了解比特幣投資市場究竟由哪些類型的投資者所組成，市場又被哪些投資者主宰。
本研究將使用Personalized PageRank演算法來評估比特幣投資者於交易網絡中的重要性，意即指節點在整體網絡中的交易量、連結數等扮演的角色及份量。我們將以RFM (Recency, Frequency, Monetary) 模型計算得出投資者的初始節點評分，並透過網絡架構令投資者的評分因交易連結傳遞。而為區隔不同類型的投資者，我們利用K-Means分群演算法以三個維度：投資者的網絡重要性、投資者擔任交易輸入方與輸出方的兩種交易模式之RFM評分，對比特幣的投資者進行分群。
本研究以區塊高度自第514,988區塊至第521,639區塊共6,652個區塊的8,383,945筆交易資料建構比特幣投資者的交易網絡，將投資者分為5個群集：活躍投資者、穩定投資者、消極投資者、潛在出場者、新進投資者，其中活躍投資者主宰了整個比特幣市場，該群集的人數為整個市場的27.4%，並作為交易網絡中的樞紐，貢獻了整個網絡60% 的重要性，更貢獻了整個市場2/3的交易量及85%的交易金額。透過本研究提出之方法，能區隔比特幣或區塊鏈相關應用上的投資者，且本研究歸納之投資者類型，將能更了解比特幣交易市場中不同類型的投資者組成。
Bitcoin is a decentralized peer-to-peer (p2p) currency system, which is the most well-known application of blockchain technology. In recent years, with the spread of the Internet and mass media, Bitcoin is favored by investors. As the result, the trading activity of Bitcoin has increased dramatically year by year, the exchange rate has also risen. Because of the accumulated record constitutes a huge trading network, we want to understand Bitcoin market through the transaction information of Bitcoin’s blockchain. We want to explore what kinds of investors construct Bitcoin investment market and which investors cluster dominates the market.
This study uses the Personalized PageRank algorithm to assess the importance of Bitcoin investors in the trading network, which means the weight of the node`s trading volume and number of links in the overall network. We will use the RFM (Recency, Frequency, Monetary) model to calculate the initial score of the investor, the investor`s score will be transmitted through the transaction link among the network structure. In order to distinguish different types of investors, the K-Means clustering algorithm is used with three dimensions: the investor`s network importance, the investor`s RFM score of different transaction modes (input/output).
In this study, we constructed a trading network graph of Bitcoin investors by blocks height 514,988 to 521,639 which have 6,652 blocks and 8,383,945 transactions. We divided investors into five clusters: active investors, stable investors, passive investors, potential abandoner, new investors. Active investors cluster dominates the market by 27.5% user amount, 60% network importance, 2/3 trading volume and 85% transaction amount of whole market. Through the methods proposed in this study, investors can be distinguished in Bitcoin or blockchain-related applications. Moreover, the investors types summarized in this study will be able to better understand the different properties of each investor cluster in the Bitcoin trading market. |
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網際網路
S. Lui, 2013. “The demographics of Bitcoin,” Simulacrum, at http://bit.ly/1FUXFru. |
| Description: | 碩士
國立政治大學
資訊管理學系
106356022 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0106356022 |
| Data Type: | thesis |
| DOI: | 10.6814/NCCU201900434 |
| Appears in Collections: | [資訊管理學系] 學位論文
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