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.

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  • A holistic approach for collaborative workload execution in volunteer clouds

    SSS. SebastioMAALASA. Scala ACM Transactions on Modeling and Computer Simulation

    Volunteer clouds

    A novel approach to volunteer clouds outperforms traditional distributed task scheduling algorithms in the presence of intensive workloads.

  • Grand canonical validation of the bipartite international trade network

    MSGCG. CaldarelliFS Physical Review E

    Bipartite trade network

    A new algorithm unveils complicated structures in the bipartite mapping between countries and products of the international trade network.

  • The price of complexity in financial networks

    SBGCG. CaldarelliRMTRJS Proceedings of the National Academy of Sciences of the USA

    The price of complexity

    Increasing the complexity of the network of contracts between financial institutions decreases the accuracy of estimating systemic risk.

  • Concurrent enhancement of percolation and synchronization in adaptive networks

    YESBGCG. Caldarelli Scientific Reports

    Self-organising adaptive networks

    An adaptive network of oscillators in fragmented and incoherent states can re-organise itself into connected and synchronized states.

  • PLoS ONE

    Collective attention to politics

    Tweet volume is a good indicator of political parties' success in elections when considered over an optimal time window so as to minimise noise.

  • Nature Physics

    Complex derivatives

    Network-based metrics to assess systemic risk and the importance of financial institutions can help tame the financial derivatives market.

  • EPL

    Clustering inverted

    Edge multiplicity—the number of triangles attached to edges—is a powerful analytic tool to understand and generalize network properties.