Under the Skin of Foundation NFT Auctions
- URL: http://arxiv.org/abs/2109.12321v1
- Date: Sat, 25 Sep 2021 09:01:44 GMT
- Title: Under the Skin of Foundation NFT Auctions
- Authors: MohammadAmin Fazli, Ali Owfi, Mohammad Reza Taesiri
- Abstract summary: We studied one of the most prominent marketplaces dedicated to NFT auctions and trades, Foundation.
We performed social network analysis on a graph that we had created based on transferred NFTs on Foundation.
We built a neural network-based similarity model for retrieving and clustering similar NFTs.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Non Fungible Tokens (NFTs) have gained a solid foothold within the crypto
community, and substantial amounts of money have been allocated to their
trades. In this paper, we studied one of the most prominent marketplaces
dedicated to NFT auctions and trades, Foundation. We analyzed the activities on
Foundation and identified several intriguing underlying dynamics that occur on
this platform. Moreover, We performed social network analysis on a graph that
we had created based on transferred NFTs on Foundation, and then described the
characteristics of this graph. Lastly, We built a neural network-based
similarity model for retrieving and clustering similar NFTs. We also showed
that for most NFTs, their performances in auctions were comparable with the
auction performance of other NFTs in their cluster.
Related papers
- Characterizing the Solana NFT Ecosystem [0.8225825738565354]
We conduct the first systematic research on the characteristics of Solana NFTs from two perspectives.
Investigating users' economic activity and NFT owner information reveals that the top users in Solana NFT are skewed toward a higher distribution of purchases.
We employ the Local Outlier Factor algorithm to conduct a wash trading audit on 2,175 popular Solana NFTs.
arXiv Detail & Related papers (2024-03-16T10:16:49Z) - Maximizing NFT Incentives: References Make You Rich [3.943871561481494]
Current Non-Fungible Token (NFT) incentive mechanisms tend to overlook their potential for scalable organizational structures.
We propose, analyze, and implement a novel reference incentive model, which is inherently structured as a Directed Acyclic Graph (DAG)-based NFT network.
arXiv Detail & Related papers (2024-02-09T15:04:16Z) - LC-TTFS: Towards Lossless Network Conversion for Spiking Neural Networks
with TTFS Coding [55.64533786293656]
We show that our algorithm can achieve a near-perfect mapping between the activation values of an ANN and the spike times of an SNN on a number of challenging AI tasks.
The study paves the way for deploying ultra-low-power TTFS-based SNNs on power-constrained edge computing platforms.
arXiv Detail & Related papers (2023-10-23T14:26:16Z) - Cryptocurrency Portfolio Optimization by Neural Networks [81.20955733184398]
This paper proposes an effective algorithm based on neural networks to take advantage of these investment products.
A deep neural network, which outputs the allocation weight of each asset at a time interval, is trained to maximize the Sharpe ratio.
A novel loss term is proposed to regulate the network's bias towards a specific asset, thus enforcing the network to learn an allocation strategy that is close to a minimum variance strategy.
arXiv Detail & Related papers (2023-10-02T12:33:28Z) - Show me your NFT and I tell you how it will perform: Multimodal
representation learning for NFT selling price prediction [2.578242050187029]
Non-Fungible Tokens (NFTs) represent deeds of ownership, based on blockchain technologies and smart contracts, of unique crypto assets on digital art forms (e.g., artworks or collectibles)
We propose MERLIN, a novel multimodal deep learning framework designed to train Transformer-based language and visual models, along with graph neural network models, on collections of NFTs' images and texts.
A key aspect in MERLIN is its independence on financial features, as it exploits only the primary data a user interested in NFT trading would like to deal with.
arXiv Detail & Related papers (2023-02-03T11:56:38Z) - Bubble or Not: Measurements, Analyses, and Findings on the Ethereum
ERC721 and ERC1155 Non-fungible Token Ecosystem [22.010657813215413]
The market capitalization of NFT reached 21.5 billion USD in 2021, almost 200 times of all previous transactions.
The rapid decline in NFT market fever in the second quarter of 2022 casts doubts on the ostensible boom in the NFT market.
By collecting data from the whole blockchain, we construct three graphs, namely NFT create graph, NFT transfer graph, and NFT hold graph, to characterize the NFT traders.
We propose new indicators to quantify the activeness and value of NFT and propose an algorithm that combines indicators and graph analyses to find bubble NFTs.
arXiv Detail & Related papers (2023-01-05T10:17:57Z) - A Game of NFTs: Characterizing NFT Wash Trading in the Ethereum Blockchain [53.8917088220974]
The Non-Fungible Token (NFT) market experienced explosive growth in 2021, with a monthly trade volume reaching $6 billion in January 2022.
Concerns have emerged about possible wash trading, a form of market manipulation in which one party repeatedly trades an NFT to inflate its volume artificially.
We find that wash trading affects 5.66% of all NFT collections, with a total artificial volume of $3,406,110,774.
arXiv Detail & Related papers (2022-12-02T15:03:35Z) - Probably Something: A Multi-Layer Taxonomy of Non-Fungible Tokens [62.997667081978825]
Non-Fungible Tokens (NFTs) are hyped and increasingly marketed as essential building blocks of the Metaverse.
This paper aims to establish a fundamental and comprehensive understanding of NFTs by identifying and structuring common characteristics within a taxonomy.
arXiv Detail & Related papers (2022-08-29T18:00:30Z) - Number Entity Recognition [65.80137628972312]
Numbers are essential components of text, like any other word tokens, from which natural language processing (NLP) models are built and deployed.
In this work, we attempt to tap this potential of state-of-the-art NLP models and transfer their ability to boost performance in related tasks.
Our proposed classification of numbers into entities helps NLP models perform well on several tasks, including a handcrafted Fill-In-The-Blank (FITB) task and on question answering using joint embeddings.
arXiv Detail & Related papers (2022-05-07T05:22:43Z) - "It's A Blessing and A Curse": Unpacking Creators' Practices with
Non-Fungible Tokens (NFTs) and Their Communities [9.270221748331096]
We focus on NFT creators and present results of an exploratory qualitative study.
Our participants had nuanced feelings about NFTs and their communities.
We discuss how the built-in properties of blockchains and NFTs might have contributed to some of these issues.
arXiv Detail & Related papers (2022-01-15T08:52:26Z) - Neural Network-based Automatic Factor Construction [58.96870869237197]
This paper proposes Neural Network-based Automatic Factor Construction (NNAFC)
NNAFC can automatically construct diversified financial factors based on financial domain knowledge.
New factors constructed by NNAFC can always improve the return, Sharpe ratio, and the max draw-down of a multi-factor quantitative investment strategy.
arXiv Detail & Related papers (2020-08-14T07:44:49Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.