{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T14:24:38Z","timestamp":1769005478697,"version":"3.49.0"},"reference-count":41,"publisher":"Association for Computing Machinery (ACM)","issue":"3","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Web"],"published-print":{"date-parts":[[2025,8,31]]},"abstract":"<jats:p>Blockchain technology and cryptocurrencies have garnered considerable attention over the past 15 years. The term Web3 (sometimes Web 3.0) has been coined to define a possible direction for the web based on the use of decentralisation via blockchain. Cryptocurrencies are characterised by high market volatility and susceptibility to substantial crashes, issues that require temporal analysis methodologies able to tackle the high temporal resolution, heterogeneity, and scale of blockchain data. While existing research attempts to analyse crash events, fundamental questions persist regarding the optimal timescale for analysis, differentiation between long-term and short-term trends, and the identification and characterisation of shock events within these decentralised systems.<\/jats:p>\n          <jats:p>This article addresses these issues by examining cryptocurrencies traded on the Ethereum blockchain, with a spotlight on the crash of the stablecoin TerraUSD (UST) and the currency LUNA designed to stabilise it. Utilising complex network analysis and a multi-layer temporal graph allows the study of the correlations between the layers representing the currencies and system evolution across diverse timescales. The investigation sheds light on the strong interconnections among stablecoins pre-crash and the significant post-crash transformations. We identify anomalous signals before, during, and after the collapse, emphasising their impact on graph structure metrics and user movement across layers.<\/jats:p>\n          <jats:p>This article is novel in its use of temporal, cross-chain graph analysis to explore a cryptocurrency collapse. It emphasises the importance of temporal analysis for studies on web-derived data. In addition, the methodology shows how graph-based analysis can enhance traditional econometric results. Overall, this research carries implications beyond its field, for example, for regulatory agencies aiming to safeguard users could use multi-layer temporal graphs as part of their suite of analysis tools.<\/jats:p>","DOI":"10.1145\/3726869","type":"journal-article","created":{"date-parts":[[2025,4,3]],"date-time":"2025-04-03T06:55:31Z","timestamp":1743663331000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Investigating the Luna-Terra Collapse through the Temporal Multilayer Graph Structure of the Ethereum Stablecoin Ecosystem"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4035-7464","authenticated-orcid":false,"given":"Cheick Tidiane","family":"Ba","sequence":"first","affiliation":[{"name":"EECS, Queen Mary University of London","place":["London, United Kingdom of Great Britain and Northern Ireland"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9446-5690","authenticated-orcid":false,"given":"Ben","family":"Steer","sequence":"additional","affiliation":[{"name":"Pometry Ltd","place":["London, United Kingdom of Great Britain and Northern Ireland"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4808-4106","authenticated-orcid":false,"given":"Matteo","family":"Zignani","sequence":"additional","affiliation":[{"name":"Computer Science, University of Milan","place":["Milano, Italy"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7241-6679","authenticated-orcid":false,"given":"Richard","family":"Clegg","sequence":"additional","affiliation":[{"name":"Queen Mary University of London Faculty of Science and Engineering","place":["London, United Kingdom of Great Britain and Northern Ireland"]}]}],"member":"320","published-online":{"date-parts":[[2025,8,22]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.3390\/fintech2010003"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bdr.2016.07.002"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/3479591"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/3501247.3531597"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.osnem.2023.100266"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1098\/rsta.2012.0375"},{"key":"e_1_3_2_8_2","article-title":"On-chain forensics: Demystifying TerraUSD De-peg","volume":"27","author":"Barthere Aurelie","year":"2022","unstructured":"Aurelie Barthere, Beili Baraki, Yong Li Khoo, Philip Grushyn, Xin Yi Lim, and Joshua Ho. 2022. 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