ArticleScientific reports2023
Integrating local and global information to identify influential nodes in complex networks.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 38 citations in OpenAlex.
- An Evaluation Method for Influential Nodes Based on Multi-Attribute Neighbor Contributions in Complex Networks.Entropy (Basel, Switzerland) · 2026Article
- IdentifyingInfluential Nodes in Complex Networks Based on the Integration of Smallest-Cycle and Non-Smallest-Cycle Features.Entropy (Basel, Switzerland) · 2026Article
- Symptom clusters and core symptoms in early postoperative esophageal cancer patients.Frontiers in gastroenterology (Lausanne, Switzerland) · 2026Article
- Identifying influential assets in higher order interdependent infrastructure networks through population impact.Scientific reports · 2025Article
- Enhanced complex network influential node detection through the integration of entropy and degree metrics with node distance.Scientific reports · 2025Article
- Topological alterations in white matter anatomical networks in cervical dystonia.BMC neurology · 2024Article
- Microbial influencers and cotton leaf curl disease (CLCuD) susceptibility: a network perspective.Frontiers in microbiology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 5 institutions in 2 countries.
Funding
Abstract
Centrality analysis is a crucial tool for understanding the role of nodes in a network, but it is unclear how different centrality measures provide much unique information. To improve the identification of influential nodes in a network, we propose a new method called Hybrid-GSM (H-GSM) that combines the K-shell decomposition approach and Degree Centrality. H-GSM characterizes the impact of nodes more precisely than the Global Structure Model (GSM), which cannot distinguish the importance of each node. We evaluate the performance of H-GSM using the SIR model to simulate the propagation process of six real-world networks. Our method outperforms other approaches regarding computational complexity, node discrimination, and accuracy. Our findings demonstrate the proposed H-GSM as an effective method for identifying influential nodes in complex networks.
Identifiers
What OpenQuestion holds
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.