ArticleNature communications2019
The structured backbone of temporal social ties.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Extracting the transitivity backbone of bipartite networks.Nature communications · 2026Article
- An evaluation tool for backbone extraction techniques in weighted complex networks.Scientific reports · 2023Article
- Spatial immunization to abate disease spreading in transportation hubs.Nature communications · 2023Article
- Contact networks have small metric backbones that maintain community structure and are primary transmission subgraphs.PLoS computational biology · 2023Article
- Characterization of interactions' persistence in time-varying networks.Scientific reports · 2023Article
- Quantifying agent impacts on contact sequences in social interactions.Scientific reports · 2022Article
- Article
- From temporal network data to the dynamics of social relationships.Proceedings. Biological sciences · 2021Article
- High-frequency trading and networked markets.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Temporal properties of higher-order interactions in social networks.Scientific reports · 2021Article
- The backbone network of dynamic functional connectivity.Network neuroscience (Cambridge, Mass.) · 2021Article
- Dynamic core-periphery structure of information sharing networks in entorhinal cortex and hippocampus.Network neuroscience (Cambridge, Mass.) · 2020Article
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
In many data sets, information on the structure and temporality of a system coexists with noise and non-essential elements. In networked systems for instance, some edges might be non-essential or exist only by chance. Filtering them out and extracting a set of relevant connections is a non-trivial task. Moreover, mehods put forward until now do not deal with time-resolved network data, which have become increasingly available. Here we develop a method for filtering temporal network data, by defining an adequate temporal null model that allows us to identify pairs of nodes having more interactions than expected given their activities: the significant ties. Moreover, our method can assign a significance to complex structures such as triads of simultaneous interactions, an impossible task for methods based on static representations. Our results hint at ways to represent temporal networks for use in data-driven models.
Indexed as
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.