ArticleScientific reports2017
Efficient collective influence maximization in cascading processes with first-order transitions.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Control of cascading failures using protective measures.Scientific reports · 2024Article
- Multi-source detection based on neighborhood entropy in social networks.Scientific reports · 2022Article
- Influencer identification in dynamical complex systems.Journal of complex networks · 2020Review
- Local floods induce large-scale abrupt failures of road networks.Nature communications · 2019Article
- Influence Maximization for Fixed Heterogeneous Thresholds.Scientific reports · 2019Article
- TSSCM: A synergism-based three-step cascade model for influence maximization on large-scale social networks.PloS one · 2019Article
- Article
- Optimal percolation on multiplex networks.Nature communications · 2017Article
- Counteracting structural errors in ensemble forecast of influenza outbreaks.Nature communications · 2017Article
- Collective Influence of Multiple Spreaders Evaluated by Tracing Real Information Flow in Large-Scale Social Networks.Scientific reports · 2016Article
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Authors and funding
5 authors.
Funding
Abstract
In many social and biological networks, the collective dynamics of the entire system can be shaped by a small set of influential units through a global cascading process, manifested by an abrupt first-order transition in dynamical behaviors. Despite its importance in applications, efficient identification of multiple influential spreaders in cascading processes still remains a challenging task for large-scale networks. Here we address this issue by exploring the collective influence in general threshold models of cascading process. Our analysis reveals that the importance of spreaders is fixed by the subcritical paths along which cascades propagate: the number of subcritical paths attached to each spreader determines its contribution to global cascades. The concept of subcritical path allows us to introduce a scalable algorithm for massively large-scale networks. Results in both synthetic random graphs and real networks show that the proposed method can achieve larger collective influence given the same number of seeds compared with other scalable heuristic approaches.
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Registered trials
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