ArticleScientific reports2018
Explicit size distributions of failure cascades redefine systemic risk on finite networks.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- The Downside of Heterogeneity: How Established Relations Counteract Systemic Adaptivity in Tasks Assignments.Entropy (Basel, Switzerland) · 2021Article
- Cascade Size Distributions: Why They Matter and How to Compute Them Efficiently.Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence · 2021Article
- Efficient message passing for cascade size distributions.Scientific reports · 2019Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
How big is the risk that a few initial failures of nodes in a network amplify to large cascades that span a substantial share of all nodes? Predicting the final cascade size is critical to ensure the functioning of a system as a whole. Yet, this task is hampered by uncertain and missing information. In infinitely large networks, the average cascade size can often be estimated by approaches building on local tree and mean field approximations. Yet, as we demonstrate, in finite networks, this average does not need to be a likely outcome. Instead, we find broad and even bimodal cascade size distributions. This phenomenon persists for system sizes up to 10
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