Complex networks are ubiquitous: a cell, the human brain, a group of people and the
Internet are all examples of interconnected many-body systems characterized by …

We argue that theories and methods drawn from complexity science are urgently needed to
guide the development and use of digital twins for cities. The theoretical framework from …

Identifying influential nodes in complex networks is a significant issue in analyzing the
spreading dynamics in networks. Many existing methods focus only on local or global …

We present a measurement study on compositions of Decentralized Finance (DeFi)
protocols, which aim to disrupt traditional finance and offer services on top of distributed …

Measuring the systemic risk contribution (SRC) of country-level stock markets helps
understand the rise of extreme risks in the worldwide stock system to prevent potential …

In this paper, we study systemic risk propagation by exploring the dynamic mechanism of
financial contagion among Eurozone countries. Using a multilayer information spillover …

Systemic risk is the risk of infection from an individual financial entity to the financial system
due to existing interconnections. Having powerful tools to analyze and predict systemic risk …

Graph learning plays a pivotal role and has gained significant attention in various
application scenarios, from social network analysis to recommendation systems, for its …

Recent advances in machine learning research have produced powerful neural graph
embedding methods, which learn useful, low-dimensional vector representations of network …

Crises like COVID-19 exposed the fragility of highly interdependent corporate supply
networks and the complex production processes depending on them. However, a …