arXiv subjects

Didier Le Bail

Publications and source records attributed to Didier Le Bail.

5 recordsLinked to original sources

arXiv2025

Generalizing Egocentric Temporal Neighborhoods to probe for spatial correlations in temporal networks and infer their topology

Motifs are thought to be some fundamental components of social face-to-face interaction temporal networks. However, the motifs previously considered are either limited to a handful of nodes and edges, or do not include triangles, which are thought to be of critical relevance to understand the dynamics of social systems. Thus, we introduce a new class of motifs, that include these triangles, are not limited in their number of nodes or edges, and yet can be mined efficiently in any temporal network. Referring to these motifs as the edge-centered motifs, we show analytically how they subsume the Egocentric Temporal Neighborhoods motifs of the literature. We also confirm in empirical data that the edge-centered motifs bring relevant information with respect to the Egocentric motifs by using a principle of maximum entropy. Then, we show how mining for the edge-centered motifs in a network can be used to probe for spatial correlations in the underlying dynamics that have produced that network. We deduce an approximate formula for the distribution of the edge-centered motifs in empirical networks of social face-to-face interactions. In the last section of this paper, we explore how the statistics of the edge-centered motifs can be used to infer the complete topology of the network they were sampled from. This leads to the needs of mathematical development, that we inaugurate here under the name of graph tiling theory.

physics.soc-ph↗

arXiv2025

Practical and scalable simulations of non-Markovian stochastic processes and temporal networks with individual node properties

Discrete stochastic processes are prevalent in natural systems, with applications in physics, biochemistry, epidemiology, sociology, and finance. While analytic solutions often cannot be derived, existing simulation frameworks can generate stochastic trajectories compatible with the dynamical laws underlying the random phenomena. Still, most simulation algorithms assume the system dynamics are memoryless (Markovian assumption), under which assumption, future occurrences only depend on the system's present state. This enables efficient and exact simulation via the Gillespie algorithm. However, many real-world systems are inherently non-Markovian and exhibit memory effects. Such systems are difficult to study analytically, and current numerical methods are often computationally expensive or limited by strong simplifying assumptions that conflict with empirical data. To address these limitations, we introduce the Rejection-based Gillespie algorithm for non-Markovian Reactions (REGIR), a general and scalable framework for simulating non-Markovian stochastic systems with arbitrary inter-event time distributions. REGIR provides user-defined accuracy while preserving the same asymptotic computational complexity as the classical Gillespie algorithm. We derive a lower bound on REGIR's approximation accuracy and demonstrate its capabilities across three representative classes of non-Markovian systems: (1) reaction channels with delays, (2) stochastic processes driven by individual reactant properties, and (3) temporal networks governed by node activity. In all cases, REGIR accurately captures memory-dependent dynamics and outperforms existing approaches in terms of flexibility and efficiency.

q-bio.QM↗

arXiv2023

Flow of temporal network properties under local aggregation and time shuffling: a tool for characterizing, comparing and classifying temporal networks

Although many tools have been developed and employed to characterize temporal networks, the issue of how to compare them remains largely open. It depends indeed on what features are considered as relevant, and on the way the differences in these features are quantified. In this paper, we propose to characterize temporal networks through their behaviour under general transformations that are local in time: (i) a local time shuffling, which destroys correlations at time scales smaller than a given scale $b$, while preserving large time scales, and (ii) a local temporal aggregation on time windows of length $n$. By varying $b$ and $n$, we obtain a flow of temporal networks, and flows of observable values, which encode the phenomenology of the temporal network on multiple time scales. We use a symbolic approach to summarize these flows into labels (strings of characters) describing their trends. These labels can then be used to compare temporal networks, validate models, or identify groups of networks with similar labels. Our procedure can be applied to any temporal network and with an arbitrary set of observables, and we illustrate it on an ensemble of data sets describing face-to-face interactions in various contexts, including both empirical and synthetic data.

physics.soc-ph↗

arXiv2023

Modeling framework unifying contact and social networks

Temporal networks of face-to-face interactions between individuals are useful proxies of the dynamics of social systems on fast time scales. Several empirical statistical properties of these networks have been shown to be robust across a large variety of contexts. In order to better grasp the role of various mechanisms of social interactions in the emergence of these properties, models in which schematic implementations of such mechanisms can be carried out have proven useful. Here, we put forward a new framework to model temporal networks of human interactions, based on the idea of a co-evolution and feedback between (i) an observed network of instantaneous interactions and (ii) an underlying unobserved social bond network: social bonds partially drive interaction opportunities, and in turn are reinforced by interactions and weakened or even removed by the lack of interactions. Through this co-evolution, we also integrate in the model well-known mechanisms such as triadic closure, but also the impact of {shared social context} and {non-intentional (casual) interactions}, with several tunable parameters. We then propose a method to compare the statistical properties of each version of the model with empirical face-to-face interaction data sets, to determine which sets of mechanisms lead to realistic social temporal networks within this modeling framework.

physics.soc-ph↗

arXiv2021

From temporal network data to the dynamics of social relationships

Networks are well-established representations of social systems, and temporal networks are widely used to study their dynamics. Temporal network data often consist in a succession of static networks over consecutive time windows whose length, however, is arbitrary, not necessarily corresponding to any intrinsic timescale of the system. Moreover, the resulting view of social network evolution is unsatisfactory: short time windows contain little information, whereas aggregating over large time windows blurs the dynamics. Going from a temporal network to a meaningful evolving representation of a social network therefore remains a challenge. Here we introduce a framework to that purpose: transforming temporal network data into an evolving weighted network where the weights of the links between individuals are updated at every interaction. Most importantly, this transformation takes into account the interdependence of social relationships due to the finite attention capacities of individuals: each interaction between two individuals not only reinforces their mutual relationship but also weakens their relationships with others. We study a concrete example of such a transformation and apply it to several data sets of social interactions. Using temporal contact data collected in schools, we show how our framework highlights specificities in their structure and temporal organization. We then introduce a synthetic perturbation into a data set of interactions in a group of baboons to show that it is possible to detect a perturbation in a social group on a wide range of timescales and parameters. Our framework brings new perspectives to the analysis of temporal social networks.

physics.soc-ph↗