Author:
(1) Prakhyat Khati, Computer Science, University of Saskatchewa, Saskatoon, Canada (khati.prakhyat@usask.com). Table of Links Abstract and 1 Introduction 2 Related Works 3 Datasets and Experiment Setup 4 Methodology 4.1 NFTs Transaction Network 4.2 NFTs Bubble Prediction 5 Discussion and Conclusions, and References 4. METHODOLOGY Our whole analysis is based on the NFTs network and its network components. So first, we need to analyze the data itself to under more about the distributed nature of the NFT network. In the fig3 below, we can see that the overwhelming number of NFT owners each only own a small number of tokens. There are very few addresses that own hundreds or even thousands of tokens. Their addresses are the collection address and are common to all the NFTs in the collection. Fig 4 is charted on a logarithmic scale for easy interpretation. This reflects the distribution of the number of tokens per address seems to follow a Zipfian distribution, as indicated in fig 4. Form the figure, any address which owns thousands of tokens is either purchasing those tokens automatically (using smart contracts) or is financing the collection in which they own the tokens. First, we analyze the ownership trends addresses that do not own many tokens. This finding will help us to estimate trends in NFT ownership. So here, some of the address contains a lot of tokens, so we select a cutoff value to be 1500. Fig5 shows that the decentralized NFT market is indeed decentralized. Most of the NFTs are digital arts now, and these digital arts get released in the collection. So, all the price information, owner information, and other metadata would be handled by a single contract account for this collection. Few of the NFTs collections are more than just a digital art. For example, NFT like “ENS” (Ethereum Name Service)[37], which behaved like a DNS, and this naming service can be used to a greater extent. NFT collection is unique, and each collection has different significance and values. This paper is available on arxiv under CC BY 4.0 DEED license. Author: (1) Prakhyat Khati, Computer Science, University of Saskatchewa, Saskatoon, Canada (khati.prakhyat@usask.com). Author: Author: (1) Prakhyat Khati, Computer Science, University of Saskatchewa, Saskatoon, Canada (khati.prakhyat@usask.com). Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Related Works 2 Related Works 3 Datasets and Experiment Setup 3 Datasets and Experiment Setup 4 Methodology 4 Methodology 4.1 NFTs Transaction Network 4.1 NFTs Transaction Network 4.2 NFTs Bubble Prediction 4.2 NFTs Bubble Prediction 5 Discussion and Conclusions, and References 5 Discussion and Conclusions, and References 4. METHODOLOGY Our whole analysis is based on the NFTs network and its network components. So first, we need to analyze the data itself to under more about the distributed nature of the NFT network. In the fig3 below, we can see that the overwhelming number of NFT owners each only own a small number of tokens. There are very few addresses that own hundreds or even thousands of tokens. Their addresses are the collection address and are common to all the NFTs in the collection. Fig 4 is charted on a logarithmic scale for easy interpretation. This reflects the distribution of the number of tokens per address seems to follow a Zipfian distribution, as indicated in fig 4. Form the figure, any address which owns thousands of tokens is either purchasing those tokens automatically (using smart contracts) or is financing the collection in which they own the tokens. First, we analyze the ownership trends addresses that do not own many tokens. This finding will help us to estimate trends in NFT ownership. So here, some of the address contains a lot of tokens, so we select a cutoff value to be 1500. Fig5 shows that the decentralized NFT market is indeed decentralized. Most of the NFTs are digital arts now, and these digital arts get released in the collection. So, all the price information, owner information, and other metadata would be handled by a single contract account for this collection. Few of the NFTs collections are more than just a digital art. For example, NFT like “ENS” (Ethereum Name Service)[37], which behaved like a DNS, and this naming service can be used to a greater extent. NFT collection is unique, and each collection has different significance and values. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

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Graph Analysis and Bubble Prediction Are Key to Understanding NFT Networks

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NFT Ownership Is Decentralized and Follows a Zipfian Distribution

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