Our papers are the official record of our discoveries. They allow others to build on and apply our work. Each paper is the result of many months of research, so we make a special effort to make them clear, beautiful and inspirational, and publish them in leading journals.
Statistical physics contributes to new models and metrics for the study of financial network structure, dynamics, stability and instability.
Networks where risky banks are mostly exposed to other risky banks have higher levels of systemic risk than those with stable bank interactions.
Naturally occurring networks have an underlying scale-free structure that is often clouded by finite-size effects in the sample data.
Consistent valuation of interbank claims within an interconnected financial system can be found with a recursive update of banks' equities.
Statistical physics harnesses links between maximum entropy and information theory to capture null model and real-world network features.
Network users who have access to the network’s most informative node, as quantified by a novel index, the InfoRank, have a competitive edge.
Bipartite networks model the structures of ecological and economic real-world systems, enabling hypothesis testing and crisis forecasting.
Bayesian networks describe the evolution of orthodontic features on patients receiving treatment versus no treatment for malocclusion.
Statistical mechanics concepts reconstruct connections between financial institutions and the stock market, despite limited data disclosure.
A new algorithm unveils complicated structures in the bipartite mapping between countries and products of the international trade network.
When people operate in echo chambers, they focus on information adhering to their system of beliefs. Debunking them is harder than it seems
Processes believed to stabilize financial markets can drive them towards instability by creating cyclical structures that amplify distress.
Non-linear models of distress propagation in financial networks characterise key regimes where shocks are either amplified or suppressed.
Targeted immunisation policies limit distress propagation and prevent system-wide crises in financial networks according to sandpile models.
Increasing the complexity of the network of contracts between financial institutions decreases the accuracy of estimating systemic risk.
Coupled distribution grids are more vulnerable to a cascading systemic failure but they have larger safe regions within their networks.
An adaptive network of oscillators in fragmented and incoherent states can re-organise itself into connected and synchronized states.
News sentiment analysis and web browsing data are unilluminating alone, but inspected together, predict fluctuations in stock prices.
A new tool derived from information theory quantitatively identifies trees, hierarchies and community structures within complex networks.
When the number of tweets about an event peaks, the sentiment of those tweets correlates strongly with abnormal stock market returns.
Analysis of the hyperbolicity of real-world networks distinguishes between those which are aristocratic and those which are democratic.
Tweet volume is a good indicator of political parties' success in elections when considered over an optimal time window so as to minimise noise.
A dynamical microscopic theory of instability for financial networks reformulates the DebtRank algorithm in terms of basic accounting principles.
The speed of a financial crisis outbreak sets the maximum delay before intervention by central authorities is no longer effective.
Time series data from networks of credit default swaps display no early warnings of financial crises without additional macroeconomic indicators.
The interplay between redundancies and smart reconfiguration protocols can improve the resilience of networked infrastructures to failures.
The optimal architecture of a financial system is only dependent on its topology when the market is illiquid, and no topology is always superior.
A new non-monetary metric captures diversification, a dominant effect on the globalised market, and the effective complexity of products.
Coupled non-linear maps extract information about the competitiveness of countries to the complexity of their products from trade data.
A new concept, graph temperature, enables the prediction of distinct topological properties of real-world networks simultaneously.
Information theory fixes weighted networks’ degeneracy issues with a generalisation of binary graphs and an optimal scale of link intensities.
Complex networks detect the driver institutions of an interbank market and ascertain that intervention policies should be time-scale dependent.
New mathematical tools can help infer financial networks from partial data to understand the propagation of distress through the network.
Network-based metrics to assess systemic risk and the importance of financial institutions can help tame the financial derivatives market.
Information about 10% of the links in a complex network is sufficient to reconstruct its main features and resilience with the fitness model.
A statistical procedure identifies dominant edges within weighted networks to determine whether a network has reached its steady state.
Network theory finds unexpected interactions between the number of products a country produces and the number of countries producing each product.
A quantitative assessment of the non-monetary advantage of diversification represents a country’s hidden potential for development and growth.
Network analysis of diagnostic data identifies combinations of the key factors which cause Class III malocclusion and how they evolve over time.
Analysis of web search queries about a given stock, from the seemingly uncoordinated activity of many users, can anticipate the trading peak.
Spectral analysis shows that disassortative networks exhibit a higher epidemiological threshold and are therefore easier to immunize.
Edge multiplicity—the number of triangles attached to edges—is a powerful analytic tool to understand and generalize network properties.