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A0368
Title: Score-driven exponential random graphs: A new class of time-varying parameter models for dynamical networks Authors:  Domenico Di Gangi - Scuola Normale Superiore di Pisa (Italy)
Giacomo Bormetti - University of Bologna (Italy) [presenting]
Fabrizio Lillo - University of Bologna and CADS Center for Analysis Decisions and Society Human Technopole Milano (Italy)
Abstract: Motivated by the evidence that real-world networks evolve in time and may exhibit non-stationary features, an extension of the Exponential Random Graph Models (ERGMs) accommodating the time variation of network parameters is proposed. Within the ERGM framework, a network realization is sampled from a static probability distribution defined parametrically in terms of network statistics. Inspired by the fast growing literature on Dynamic Conditional Score-driven models, each parameter evolves according to an updating rule driven by the score of the conditional distribution. We demonstrate the flexibility of the score-driven ERGMs, both as data generating processes and as filters, and we prove the advantages of the dynamic version with respect to the static one. The proposed method captures dynamical network dependencies, that emerge from the data, and allows for a test discriminating between static or time-varying parameters. Finally, we corroborate our findings with the application to networks from real financial and political systems exhibiting non-stationary dynamics.