Domenico Di Gangi, Giacomo Bormetti, Fabrizio Lillo
arXiv 26 May 2019 · Statistics — Applications · publishedChaos An Interdisciplinary Journal of Nonlinear Science (2024) · 3 citations (OpenAlex)
arXiv:1905.10806 · PDF · DOI · OpenAlex · Extracted main text
Motivated by the increasing abundance of data describing real-world networks that exhibit dynamical features, we propose an extension of the Exponential Random Graph Models (ERGMs) that accommodates the time variation of its parameters. Inspired by the fast-growing literature on Dynamic Conditional Score models, each parameter evolves according to an updating rule driven by the score of the ERGM distribution. We demonstrate the flexibility of score-driven ERGMs (SD-ERGMs) as data-generating processes and filters and show the advantages of the dynamic version over the static one. We discuss two applications to temporal networks from financial and political systems. First, we consider the prediction of future links in the Italian interbank credit network. Second, we show that the SD-ERGM allows discriminating between static or time-varying parameters when used to model the U.S. Congress co-voting network dynamics.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | \@secondoftwo author \@secondoftwo author G. Buccheri, \@secondoftwo… (2018) \@secondoftwo title Filtering and smoothing with score-driven models | 0.843 | 4 | 3 | 75% |
| 2 | \@secondoftwo author \@secondoftwo author S. Chatterjee, \@secondoft… (2011) \@secondoftwo title Random graphs with a given degree sequence | 0.830 | 7 | 4 | 57% |
| 3 | \@secondoftwo author \@secondoftwo author D. R.\ Hunter, \@secondoft… (2008) \@secondoftwo title ergm: A package to fit, simulate and diagnose exponential-family models for networks | 0.737 | 4 | 3 | 50% |
| 4 | \@secondoftwo author \@secondoftwo author D. Creal, \@secondoftwo au… (2013) \@secondoftwo title Generalized autoregressive score models with applications | 0.737 | 3 | 3 | 67% |
| 5 | \@secondoftwo author \@secondoftwo author J. Lee, \@secondoftwo auth… (2020) \@secondoftwo title Varying-coefficient models for dynamic networks | 0.737 | 3 | 2 | 100% |
| 6 | \@secondoftwo author \@secondoftwo author F. Calvori, \@secondoftwo… (2017) \@secondoftwo title Testing for parameter instability across different modeling frameworks | 0.644 | 5 | 2 | 40% |
| 7 | \@secondoftwo author \@secondoftwo author J. Park\ and\ \@secondoftw… (2004) \@secondoftwo title Statistical mechanics of networks | 0.644 | 3 | 2 | 67% |
| 8 | \@secondoftwo author \@secondoftwo author D. Garlaschelli\ and\ \@se… (2008) \@secondoftwo title Maximum likelihood: Extracting unbiased information from complex networks | 0.644 | 3 | 2 | 67% |
| 9 | \@secondoftwo author \@secondoftwo author D. R.\ Cox, \@secondoftwo… (1981) \@secondoftwo title Statistical analysis of time series: Some recent developments | 0.644 | 2 | 2 | 100% |
| 10 | \@secondoftwo author \@secondoftwo author S. J.\ Cranmer\ and\ \@sec… (2011) \@secondoftwo title Inferential network analysis with exponential random graph models | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 84 scored citations.