ArticleScientific reports2019
Information diffusion backbones in temporal networks.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- A Community Detection Model Based on Dynamic Propagation-Aware Multi-Hop Feature Aggregation.Entropy (Basel, Switzerland) · 2025Article
- Temporal-topological properties of higher-order evolving networks.Scientific reports · 2023Article
- Contact networks have small metric backbones that maintain community structure and are primary transmission subgraphs.PLoS computational biology · 2023Article
- Sharing Information about Health-Related Resources: Observations from a Community Resource Referral Intervention Trial in a Predominantly African American/Black Community.Journal of the Association for Information Science and Technology · 2022Article
- Habituation effect in social networks as a potential factor silently crushing influence maximisation efforts.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Progress has been made in understanding how temporal network features affect the percentage of nodes reached by an information diffusion process. In this work, we explore further: which node pairs are likely to contribute to the actual diffusion of information, i.e., appear in a diffusion trajectory? How is this likelihood related to the local temporal connection features of the node pair? Such deep understanding of the role of node pairs is crucial to tackle challenging optimization problems such as which kind of node pairs or temporal contacts should be stimulated in order to maximize the prevalence of information spreading. We start by using Susceptible-Infected (SI) model, in which an infected (information possessing) node could spread the information to a susceptible node with a given infection probability β whenever a contact happens between the two nodes, as the information diffusion process. We consider a large number of real-world temporal networks. First, we propose the construction of an information diffusion backbone G
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.