ArticleScientific reports2016
Input graph: the hidden geometry in controlling complex networks.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Module control of network analysis in psychopathology.iScience · 2024Article
- Total network controllability analysis discovers explainable drugs for Covid-19 treatment.Biology direct · 2023Article
- Cancer-keeper genes as therapeutic targets.iScience · 2023Article
- Dilations and degeneracy in network controllability.Scientific reports · 2021Article
- Control Analysis of Protein-Protein Interaction Network Reveals Potential Regulatory Targets for MYCN.Frontiers in oncology · 2021Article
- Network Controllability-Based Prioritization of Candidates for SARS-CoV-2 Drug Repositioning.Viruses · 2020Article
- A dual controllability analysis of influenza virus-host protein-protein interaction networks for antiviral drug target discovery.BMC bioinformatics · 2019Article
- An efficient algorithm for finding all possible input nodes for controlling complex networks.Scientific reports · 2017Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The ability to control a complex network towards a desired behavior relies on our understanding of the complex nature of these social and technological networks. The existence of numerous control schemes in a network promotes us to wonder: what is the underlying relationship of all possible input nodes? Here we introduce input graph, a simple geometry that reveals the complex relationship between all control schemes and input nodes. We prove that the node adjacent to an input node in the input graph will appear in another control scheme, and the connected nodes in input graph have the same type in control, which they are either all possible input nodes or not. Furthermore, we find that the giant components emerge in the input graphs of many real networks, which provides a clear topological explanation of bifurcation phenomenon emerging in dense networks and promotes us to design an efficient method to alter the node type in control. The findings provide an insight into control principles of complex networks and offer a general mechanism to design a suitable control scheme for different purposes.
Identifiers
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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.