Data depth and core-based trend detection on blockchain transaction networks
Blockchains are significantly easing trade finance, with billions of dollars worth of assets being transacted daily. However, analyzing these networks remains challenging due to the sheer volume and complexity of the data. We introduce a method named InnerCore that detects market manipulators within...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-02-01
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| Series: | Frontiers in Blockchain |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fbloc.2024.1342956/full |
| _version_ | 1797305205170110464 |
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| author | Jason Zhu Arijit Khan Cuneyt Gurcan Akcora |
| author_facet | Jason Zhu Arijit Khan Cuneyt Gurcan Akcora |
| author_sort | Jason Zhu |
| collection | DOAJ |
| description | Blockchains are significantly easing trade finance, with billions of dollars worth of assets being transacted daily. However, analyzing these networks remains challenging due to the sheer volume and complexity of the data. We introduce a method named InnerCore that detects market manipulators within blockchain-based networks and offers a sentiment indicator for these networks. This is achieved through data depth-based core decomposition and centered motif discovery, ensuring scalability. InnerCore is a computationally efficient, unsupervised approach suitable for analyzing large temporal graphs. We demonstrate its effectiveness by analyzing and detecting three recent real-world incidents from our datasets: the catastrophic collapse of LunaTerra, the Proof-of-Stake switch of Ethereum, and the temporary peg loss of USDC–while also verifying our results against external ground truth. Our experiments show that InnerCore can match the qualified analysis accurately without human involvement, automating blockchain analysis in a scalable manner, while being more effective and efficient than baselines and state-of-the-art attributed change detection approach in dynamic graphs. |
| first_indexed | 2024-03-08T00:21:33Z |
| format | Article |
| id | doaj.art-04f6c618302a44acbdd2c8d3e8f4532d |
| institution | Directory Open Access Journal |
| issn | 2624-7852 |
| language | English |
| last_indexed | 2024-03-08T00:21:33Z |
| publishDate | 2024-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Blockchain |
| spelling | doaj.art-04f6c618302a44acbdd2c8d3e8f4532d2024-02-16T04:35:53ZengFrontiers Media S.A.Frontiers in Blockchain2624-78522024-02-01710.3389/fbloc.2024.13429561342956Data depth and core-based trend detection on blockchain transaction networksJason Zhu0Arijit Khan1Cuneyt Gurcan Akcora2Department of Computer Science, University of Manitoba, Winnipeg, CanadaDepartment of Computer Science, Aalborg University, Aalborg, DenmarkDepartment of Computer Science, University of Central Florida, Orlando, FL, United StatesBlockchains are significantly easing trade finance, with billions of dollars worth of assets being transacted daily. However, analyzing these networks remains challenging due to the sheer volume and complexity of the data. We introduce a method named InnerCore that detects market manipulators within blockchain-based networks and offers a sentiment indicator for these networks. This is achieved through data depth-based core decomposition and centered motif discovery, ensuring scalability. InnerCore is a computationally efficient, unsupervised approach suitable for analyzing large temporal graphs. We demonstrate its effectiveness by analyzing and detecting three recent real-world incidents from our datasets: the catastrophic collapse of LunaTerra, the Proof-of-Stake switch of Ethereum, and the temporary peg loss of USDC–while also verifying our results against external ground truth. Our experiments show that InnerCore can match the qualified analysis accurately without human involvement, automating blockchain analysis in a scalable manner, while being more effective and efficient than baselines and state-of-the-art attributed change detection approach in dynamic graphs.https://www.frontiersin.org/articles/10.3389/fbloc.2024.1342956/fullblockchain networksdecentralized financestablecoindata depthcore decompositionnetwork motifs |
| spellingShingle | Jason Zhu Arijit Khan Cuneyt Gurcan Akcora Data depth and core-based trend detection on blockchain transaction networks Frontiers in Blockchain blockchain networks decentralized finance stablecoin data depth core decomposition network motifs |
| title | Data depth and core-based trend detection on blockchain transaction networks |
| title_full | Data depth and core-based trend detection on blockchain transaction networks |
| title_fullStr | Data depth and core-based trend detection on blockchain transaction networks |
| title_full_unstemmed | Data depth and core-based trend detection on blockchain transaction networks |
| title_short | Data depth and core-based trend detection on blockchain transaction networks |
| title_sort | data depth and core based trend detection on blockchain transaction networks |
| topic | blockchain networks decentralized finance stablecoin data depth core decomposition network motifs |
| url | https://www.frontiersin.org/articles/10.3389/fbloc.2024.1342956/full |
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