Summary of Review Of Blockchain Application with Graph Neural Networks, Graph Convolutional Networks and Convolutional Neural Networks, by Amy Ancelotti et al.
Review of blockchain application with Graph Neural Networks, Graph Convolutional Networks and Convolutional Neural Networks
by Amy Ancelotti, Claudia Liason
First submitted to arxiv on: 1 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A medium-difficulty summary: This paper reviews the applications of Graph Neural Networks (GNNs), Graph Convolutional Networks (GCNs), and Convolutional Neural Networks (CNNs) in blockchain technology. Traditional analytical methods are inadequate in capturing complex relationships and behaviors of decentralized systems, prompting the use of deep learning models to leverage graph-based and temporal structures inherent in blockchain architectures. GNNs and GCNs excel in modeling relational data of blockchain nodes and transactions, making them suitable for fraud detection, transaction verification, and smart contract analysis. CNNs can analyze blockchain data when represented as structured matrices, revealing hidden patterns in transaction flows. The paper explores how these models enhance efficiency, security, and scalability of linear blockchains and DAG-based systems, highlighting their strengths and future research directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A low-difficulty summary: This paper looks at using special kinds of artificial intelligence called Graph Neural Networks, Graph Convolutional Networks, and Convolutional Neural Networks to improve blockchain technology. Blockchain is a way for people to store and share information securely. The problem with current methods is that they’re not very good at understanding complex relationships between different parts of the blockchain. These new AI models are better because they can understand these relationships and use them to detect fraud, verify transactions, and analyze smart contracts. The paper also talks about how these models can make blockchain technology more efficient, secure, and fast. |
Keywords
» Artificial intelligence » Deep learning » Prompting
