Domenico Di Gangi, Giacomo Bormetti, Fabrizio Lillo
arXiv 20 Feb 2022 · Statistics — Applications · publishedInformation Sciences (2022) · 2 citations (OpenAlex)
arXiv:2202.09854 · PDF · DOI · OpenAlex · Extracted main text
While the vast majority of the literature on models for temporal networks focuses on binary graphs, often one can associate a weight to each link. In such cases the data are better described by a weighted, or valued, network. An important well known fact is that real world weighted networks are typically sparse. We propose a novel time varying parameter model for sparse and weighted temporal networks as a combination of the fitness model, appropriately extended, and the score driven framework. We consider a zero augmented generalized linear model to handle the weights and an observation driven approach to describe time varying parameters. The result is a flexible approach where the probability of a link to exist is independent from its expected weight. This represents a crucial difference with alternative specifications proposed in the recent literature, with relevant implications for the flexibility of the model. Our approach also accommodates for the dependence of the network dynamics on external variables. We present a link forecasting analysis to data describing the overnight exposures in the Euro interbank market and investigate whether the influence of EONIA rates on the interbank network dynamics has changed over time.
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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 | L. Giraitis, G. Kapetanios, A. Wetherilt, F. Zikes, Estimating the d… (2016) 58–84 (2016) | 1.000 | 7 | 3 | 100% |
| 2 | D. Di Gangi, G. Bormetti, F. Lillo, Score-driven exponential random… (2019) | 1.000 | 6 | 3 | 100% |
| 3 | P. Mazzarisi, P. Barucca, F. Lillo, D. Tantari, A dynamic network mo… (2020) 50–65 (2020) | 0.956 | 8 | 3 | 88% |
| 4 | C. Brunetti, J. H. Harris, S. Mankad, G. Michailidis, Interconnected… (2019) 520–538 (2019) | 0.874 | 6 | 2 | 100% |
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| 7 | V. Hatzopoulos, G. Iori, R. N. Mantegna, S. Miccichè, M. Tumminello,… (2015) 693–710 (2015) | 0.874 | 5 | 2 | 100% |
| 8 | A. C. Harvey, Dynamic models for volatility and heavy tails: with ap… (2013) | 0.811 | 4 | 2 | 100% |
| 9 | T. Yan, C. Leng, J. Zhu, et al., Asymptotics in directed exponential… (2016) 31–57 (2016) | 0.737 | 4 | 3 | 50% |
| 10 | T. Yan, B. Jiang, S. E. Fienberg, C. Leng, Statistical inference in… (2019) 857–868 (2019) | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 74 scored citations.