# The structured backbone of temporal social ties

@article{Kobayashi2019TheSB, title={The structured backbone of temporal social ties}, author={Teruyoshi Kobayashi and Taro Takaguchi and Alain Barrat}, journal={Nature Communications}, year={2019}, volume={10} }

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…

## 37 Citations

From temporal network data to the dynamics of social relationships

- Computer SciencebioRxiv
- 2021

This work introduces a framework to transforming temporal network data into an evolving weighted network where the weights of the links between individuals are updated at every interaction, and takes into account the interdependence of social relationships due to the finite attention capacities of individuals.

From temporal network data to the dynamics of social relationships

- Computer ScienceProceedings of the Royal Society B
- 2021

This work presents a new framework to study the dynamic evolution of social networks based on the idea that social relationships are interdependent, and implements this interdependence in a parsimonious two-parameter model and applies it to several human and non-human primates’ datasets to demonstrate that this model detects even small and short perturbations of the networks that cannot be detected using the standard technique of successive aggregated networks.

Building surrogate temporal network data from observed backbones.

- Computer SciencePhysical review. E
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The results give hints on how to best summarize complex data sets so that they remain actionable and show how ensembles of surrogate data with similar properties can be obtained from an original single data set, without any modeling assumptions.

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- Computer Science
- 2022

This work proposes a numerical maximum-likelihood method to estimate population size and the probability of two nodes connecting at any given point in time, and enables the simultaneous (rather than the asynchronous) contribution of each mechanism in the densification and sparsification of human contacts, providing a better understanding of how humans collectively construct and deconstruct social networks.

Detecting network backbones against time variations in node properties

- Computer ScienceNonlinear Dynamics
- 2019

It is shown that neglecting time variations in node-specific properties may beget false positives in the inference of the network backbone, and the viability of the proposed approach to aid in the discovery of network backbones from time series is demonstrated.

Detecting informative higher-order interactions in statistically validated hypergraphs

- Computer ScienceCommunications Physics
- 2021

This work proposes an analytic approach to filter hypergraphs by identifying those hyperlinks that are over-expressed with respect to a random null hypothesis, and represent the most relevant higher-order connections.

Temporal properties of higher-order interactions in social networks

- SociologyScientific reports
- 2021

This work investigates the higher-order organizations of temporal social networks by analyzing five publicly available datasets collected in different social settings and finds that higher- order interactions are ubiquitous and, similarly to their pairwise counterparts, characterized by heterogeneous dynamics.

Reconstructing irreducible links in temporal networks: which tool to choose depends on the network size

- Computer ScienceJournal of Physics: Complexity
- 2020

This work examines the performance of three approaches for the discovery of backbone networks, in the presence of heterogeneous, time-varying node properties, and proposes general guidelines for the use of these three approaches based on the available dataset.

The Backbone Network of Dynamic Functional Connectivity

- Computer SciencebioRxiv
- 2021

This work proposes a data-driven procedure to reveal the irreducible ties in dynamic functional connectivity of resting state fRMI data with continuous weights that includes a null model that estimates the latent characteristics of the distributions of temporal links through optimization, followed by a statistical test to filter the links whose formation can be reduced to the activities and local properties of their interacting nodes.

The Backbone Network of Dynamic Functional Connectivity : 2

- Computer Science
- 2021

This work proposes a data-driven procedure to reveal the irreducible ties in dynamic functional connectivity of resting state fRMI data with continuous weights, and proposes an optimization-based null model to infer the significant ties.

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