Conference Agenda

The cryptocurrency market has grown exponentially to become a significant part of the global financial system, with a market capitalisation of $3.05 trillion as of 26 February 2025. Designed to maintain a stable value pegged to a reserve asset, stablecoins have gained momentum among traders and investors as a bridge between traditional fiat currencies and the decentralized world of cryptocurrencies.

Despite the perceived stability provided by built-in decentralised algorithms and their relative autonomy from fundamentals, stablecoin networks are not immune to critical events, with adaptive responses not yet fully understood. This study examines how the transaction networks of two Ethereum-based stablecoins adapt in response to the Terra-Luna crisis, one of the most tragic panic crises in the cryptocurrency world. By using Relational Event Models (REMs), we study the evolution of these stablecoin network structures and the mechanisms driving their post-shock adaptation. In particular, our analysis explores the role of triadic closure as a potential stabilising mechanism and investigates whether the formation of new triadic connections has shaped the overall structure during the crisis period and helped to restore network cohesion after disruptions.

Our REM analysis reveals a shift in network dynamics after the crisis. Under stable conditions, tie formation is mainly driven by reciprocation and transitive closure, while after the shock these mechanisms weaken significantly, suggesting a disruption of established relational patterns. Cyclical closure becomes more prominent, suggesting a shift towards alternative reconfiguration strategies to maintain network cohesion. Using REMs, this study provides one of the first researches on how stablecoin networks adapt in response to critical events and demonstrates the importance of these models for studying network dynamics and formation mechanisms, with potential implications for research on the microstructure of financial markets.

Empirical contact networks remain underexploited in revealing fundamental mechanisms driving social behaviours. We propose an original modeling framework for generating temporal networks, designed to reproduce key observables from empirical data [1]. Our hypothesis is that some of these observed features are intrinsically linked to the spatial constraints of face-to-face interactions. Unlike conventional network-based approaches, our models incorporate the critical role of spatial constraints in shaping interaction patterns. This approach builds on the core idea of Starnini et al. [2], but shifts to a more fundamental framework by considering social homogeneity in agents behaviour.

Starting from a pedestrian model with continuous space and discrete time, a ”contact” occurs when two agents face each other within a certain radius. A temporal network is constructed with nodes representing agents and links defined by these contacts. Our simulations explore various dynamics, interaction mechanisms, boundary conditions, and spatial configurations to assess the geometry's impact. The resulted temporal networks are then compared against empirical time-varying networks collected during four conferences [3].

One key result concerns the inter-contact duration distribution, which is power-law distributed with an exponent −3/2 in the empirical data. We reproduce this result using three different pedestrian models: two-dimensional random walk; active Brownian particles; and the Vicsek model [1]. This suggests that this property can be recovered by any pedestrian dynamics as soon as it has a random underlying mechanism.

We believe that this novel approach to network generation offers a framework to link observations on temporal networks to sociological interpretations.