NEXT:
16 May 2024 | h 14:30 | @Sala consiglio 8th floor

Talk 1

Investigating shocking events through temporal multilayer graph structure

Cheick Ba

Cryptocurrencies have gained attention for their substantial monetary value, although they are plagued by issues such as high volatility in value and susceptibility to sudden crashes. These critical issues have spurred the interest in temporal analysis methodologies to analyze crash events, leveraging blockchain data. Indeed, there are several open research questions regarding crash events, that are difficult to address, as we require methodologies for the analysis that tackle the high temporal resolution, heterogeneity and scale of the data. We employ multi-layer, temporal network analysis techniques facilitated by Raphtory software, developed by Queen Mary University of London and Pometry Ltd. This software enables simultaneous examination of how temporal networks evolve over time and at various scales, considering multiple network layers concurrently. We introduce a network-based methodology for investigating a crash event, and applying to investigate the crash of the stablecoin TerraUSD (USTC) and its stabilizing currency LUNA (WLUNC), applying it to a dataset of transactions covering them and other cryptocurrencies traded on the Ethereum blockchain. The temporal and multilayer approach unveils pre-crash correlations among stablecoins and significant postcrash transformations. Moreover, the layer-by-layer analysis led to the detection of anomalous signals before and after the currency’s crash, as well as an analysis of changes in both graph structure metrics and user currency preferences. In addition, the temporal approach, allows for a detailed analysis of specific time intervals, highlighting coordinated behaviour among users contributing to anomalous peaks. This talk emphasizes the importance of multi-layer, temporal analysis of web-derived data. It also demonstrates how graph-based techniques can enhance traditional econometric statistics. The results of a network-based analysis can aid regulatory agencies in safeguarding users from monetary shocks and monitoring investment risks for citizens and investors.

Talk 2

Temporal triadic closure patterns in online social networks

Alessia Galdeman

Online social platforms for digital communication necessitate an in-depth understanding of their evolving dynamics, especially after the renewal requests brought about by new paradigms, such as Web3. The dynamics within online social networks (OSNs) are influenced by numerous factors, encompassing user behavior, content generation, platform features, and technological advancements, with triadic closure standing out as a prominent and influential element. In this study, we focus on the temporal aspects of triadic closure and its role in the evolution of OSNs. By analyzing temporal networks from diverse platforms based on different architectures, including communication, Web3-based, and trade networks, we developed a comprehensive analytical pipeline to support the study of triadic closure patterns. This pipeline includes an efficient algorithm for the census of temporal triads, a vector-based model for representing temporal networks (temporal triadic profile), the identification of triadic closure rules (TERs), and the evaluation of the speed of the formation of the predictability of evolutionary patterns based on triads. This study not only enhances the comprehension of triadic closure in the temporal evolution of OSNs but also provides valuable insights to be taken into account for the design and administration of online social platforms.