chains-project / scsc

smart contract supply chain
https://chains.proj.kth.se/scsc
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Network of smart contracts #11

Open Mokita-J opened 1 month ago

Mokita-J commented 1 month ago

On the Ethereum blockchain structure: A complex networks theory perspective 2019 Journal Concurrency and Computation: Practice and Experience Network of Ethereum Accounts (nodes) and its interactions (edges). The interactions considered are the transactions persisted on the blockchain (internal transactions were not considered). Tool: EtherNet Galaxy

Mokita-J commented 1 month ago

DApps Ecosystems: Mapping the Network Structure of Smart Contract Interactions 2024 arxiv paper Network of Smart contracts (nodes) and their function calls (edges). Function calls are detected through static analysis on the source code of dApps. Dataset: 66 dApps written in Solidity. Tool used: MindTheDapp

Results:

Thanks @Mokita-J @Stamp9

Mokita-J commented 1 month ago

Bubblemaps First supply audit for Tokens and NFTs. It shows a network per token. This network consists of token owners (nodes) and ownership transfers (edges). By visualizing and analyzing each token network, Bubblemaps argues that it's possible to detect market manipulation techniques such as wash trading.

The target of this supply transparency tool is crypto investors.

interview https://www.youtube.com/watch?v=Z_52VunVQnk

Speak https://ethcc.io/archive/Is-Privacy-Gone-What-Blockchain-Data-Reveals

NFT wash trading literature

Stamp9 commented 1 month ago

Transaction graph based key node identification for blockchain regulation https://link.springer.com/article/10.1007/s12083-024-01783-y

summary

1. Node screening pre‑detection

Hierarchical culling the k-shell graph decomposition algorithm for hierarchical operations, obtaining nodes distributed across different k-shell layers

Filtering operation For each layer of nodes commences with an information entropy calculation coupled with an evaluation of their clustering coefficient

Pre-detection By systematically identifying and scanning the remaining nodes, calculating the similarity between nodes, and evaluating the structural importance of each node concerning its neighbors, discern a set of core nodes.

2. Local Community Expansion

Latent Factor Model (LFM) for community expansion.

3. Node Importance Ranking LeaderRank algorithm is employed for importance ranking