Author:
(1) Prakhyat Khati, Computer Science, University of Saskatchewa, Saskatoon, Canada ([email protected]).
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
5. DISCUSSION AND CONCLUSIONS
In this work, we investigate the NFT transaction graphs and analyze the different global network properties e.g., reciprocity, assortativity, core decomposition, and clustering coefficient, we realize how their connections change. We discussed what these network properties resemble in such a network. We observed the log rank of token owners and found it to be following Zipf’s law which is generally understood to be a power-law distribution with integer values. Many real graphs for social media and the internet show existence of (several)high degree nodes. For example, several social networks have influencer nodes, and in web networks, they are popular and high-ranked websites. These all kinds of networks have been confirmed to follow power laws in their degree distribution. We observed high influencer nodes in the network and community being formed around them, indicating single hub dominating the network. The single hub is the NFT collection owner who are frequently trading. In face in [5].it has been shown that a single hub frequently dominates individual networks for ERC-20 tokens. Article ranking was used to identify such influencer nodes and Gephi was used to plot the community formed around such nodes. This led to the conclusion that the structure of NFT network is qualitatively very similar to the one measured for interaction in social network.
In the fig 12 below, overall, the LPPL model seems to capture the the trend of both the positive and negative bubble for CryptoPunks collection. In the result, although the price rose further after bubbleindicator(pos) reached its highest in late August 2020. The model is successful in predicting the direction of price change but around October 2020 to March 2021 here the bubbleindicator(pos) failed to predict the continuous price increase Similarly, from the fig 12, near December 20,2021 the bubble indicators are signaling negative value ~ 0.3. This implies that NFT collection are in general, a small bubble (predicting price increase).
This study also shed quantitative light on a NFT market that might otherwise be prone to hype and misleading information.
Future work
Following our characterization of the NFT network, there is ample opportunity for future work. Study the nature of NFT collection, identify what distinguishes the NFTs collection and what adds on value to those collections. Identifying entropy of ownership could be one of many ways to capture utility of NFT collection. Similar Further research can be used to refine our knowledge and understand the relation between blockchain platforms and NFT collections specific to those platforms. Study the temporal nature of network community formation of NFTs and predict the nature of the communities Similarly bubble prediction of the whole NFT collection can be done. Also, in the current time-series we assumed the CryptoPunks collection to be homogeneous nature, but all these NFTs are heterogeneous.
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