Dataset of Linkability Networks of Ethereum Accounts Involved in NFT Trading of Top 15 NFT Collections
Round 1
Reviewer 1 Report
Authors presented a dataset of linkability networks of ethereum accounts. The paper doesn't have research merit. Mere description of graph from features collected from multiple networks doesn't mean anything.
What kind of threat model are you using in this paper?
it is not evident from the summary what analytics can be performed on this dataset to identify the fraudulent trading accounts.
How can you formally define these linkability subgraphs?
There are no formal definitions of terms used, no experimental evaluation or algorithmic pseudocode to show practical applications of this data collected.
Can be improved. Please review any syntactic and grammatical issues.
Author Response
Please see the attachment.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
The article “Dataset of Linkability Networks of Ethereum Accounts Involved in NFT trading of Top 15 NFT Collections” e present subgraphs of Ethereum wallets involved in NFT trades of the 1 top 15 ERC721 NFT collections.
I found the summary very poor. The authors need to add further detail and other references to support the necessity of this article. Therefore, I listed some references below to improve the soundness of your article.
The origin of data is well described but unclear the several steps and parameters used to filter and collect data. Authors need to explain all the variables considered and fixed to obtain the database.
It is not clear that your quality and noise measures are adopted in the check of your dataset.
Finally, proofreading in different parts is needed.
Good luck!
References:
Abbate et al., 2022. "Investigating Healthcare 4.0 Transition Through a Knowledge Management Perspective," IEEE Transactions on Engineering Management. DOI: 10.1109/TEM.2022.3200889.
Taherdoost and Madanchian, 2023. Blockchain-Based New Business Models: A Systematic Review, Electronics (Switzerland), 12 (6), art. no. 1479. DOI: 10.3390/electronics12061479.
Bonifazi et al., 2023. Performing Wash Trading on NFTs: Is the Game Worth the Candle? (2023) Big Data and Cognitive Computing, 7 (1), art. no. 38. DOI: 10.3390/bdcc7010038.
Minor editing of English language required
Author Response
Please see the attachment.
Author Response File: Author Response.pdf
Reviewer 3 Report
Thank you for providing the opportunity to review the article titled “Dataset of Linkability Networks of Ethereum Accounts Involved in NFT trading of Top 15 NFT Collections”.
Here are some suggestions to improve the paper:
Background and Context: Provide a brief background on the rise of NFTs (Non-Fungible Tokens) and their significance in the Ethereum ecosystem. Explain why understanding the linkability of accounts involved in NFT trading is important for issues like user privacy, transaction analysis, or market behavior.
Methodology: Expand on the methodology used to obtain the subgraphs of Ethereum wallets involved in NFT trades. Describe the specific steps taken to extract the Ethereum transaction graph from a live Ethereum node and the criteria used for filtering out exchanges, mining pools, and smart contracts. Additionally, provide more details on the process of identifying the set of accounts involved in NFT trading for each selected collection and how the breadth-first search was conducted.
Contribution and Significance: Elaborate on the potential insights and implications of the obtained subgraphs. Explain how these subgraphs can offer valuable information regarding the linkability of accounts participating in NFT trading on the Ethereum blockchain. Discuss the potential applications of this analysis, such as understanding the behavior of specific NFT collections, identifying patterns in trading activities, or assessing the overall network structure.
Limitations and Future Work: Address any limitations or challenges encountered during the research process. Highlight areas for improvement and suggest future directions for research, such as exploring additional graph analysis techniques, considering different filtering criteria, or investigating the impact of different types of transactions on the linkability of accounts.
By incorporating these suggestions, you can provide a clearer and more comprehensive overview of your research, its methodology, and its contributions to the field of NFT trading analysis.
Other issue:
According to the author ”..The objective of the paper is to publish data that would aid efforts of fellow scholars in identifying possible wash trading, fraud detection and general network analysis of large networks..” I strongly suggest explaining it in more detail.
Table 2 format should be an alliance with the journal format.
Comments for author File: Comments.pdf
Author Response
Please see the attachment.
Author Response File: Author Response.pdf
Reviewer 4 Report
As no data provided, I could not check the actual data and its structure as well. Based on the description from the paper, it would be better if authors could provide more graphs and insights about data collected. A conclusion should be added as well.
Author Response
Please see the attachment.
Author Response File: Author Response.pdf
Round 2
Reviewer 1 Report
Authors have made appropriate revisions to address concerns from previous round. Paper can be accepted in current form.
Reviewer 4 Report
The revised paper is good to be published.
