ArticleScientific reports2018
Fast and accurate detection of spread source in large complex 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 8 papers.
What it found
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Who cites it
8 citing papers in PubMed.
- The Big Bang of an epidemic: a metapopulation approach to identify the spatiotemporal origin of contagious diseases and their universal spreading pattern.Scientific reports · 2025Article
- Diffusion characteristics classification framework for identification of diffusion source in complex networks.PloS one · 2023Article
- Source identification via contact tracing in the presence of asymptomatic patients.Applied network science · 2023Article
- Trading contact tracing efficiency for finding patient zero.Scientific reports · 2022Article
- Social diffusion sources can escape detection.iScience · 2022Article
- Comparison of observer based methods for source localisation in complex networks.Scientific reports · 2022Article
- New Insights Into the Social Rumor Characteristics During the COVID-19 Pandemic in China.Frontiers in public health · 2022Article
- Finding disease outbreak locations from human mobility data.EPJ data science · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Spread over complex networks is a ubiquitous process with increasingly wide applications. Locating spread sources is often important, e.g. finding the patient one in epidemics, or source of rumor spreading in social network. Pinto, Thiran and Vetterli introduced an algorithm (PTVA) to solve the important case of this problem in which a limited set of nodes act as observers and report times at which the spread reached them. PTVA uses all observers to find a solution. Here we propose a new approach in which observers with low quality information (i.e. with large spread encounter times) are ignored and potential sources are selected based on the likelihood gradient from high quality observers. The original complexity of PTVA is O(N
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Registered trials
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