What's in a crowd? Analysis of face-to-face behavioral networks

By Lorenzo Isella, Juliette Stehlé, Alain Barrat, Ciro Cattuto, Jean-François Pinton, Wouter Van den Broeck. Journal of Theoretical Biology, 2010

872 citations recorded by the source corpus.

The availability of new data sources on human mobility is opening new avenues for investigating the interplay of social networks, human mobility and dynamical processes such as epidemic spreading. Here we analyze data on the time-resolved face-to-face proximity of individuals in large-scale real-world scenarios. We compare two settings with very different properties, a scientific conference and a long-running museum exhibition. We track the behavioral networks of face-to-face proximity, and characterize them from both a static and a dynamic point of view, exposing differences and similarities. We use our data to investigate the dynamics of a susceptible-infected model for epidemic spreading that unfolds on the dynamical networks of human proximity. The spreading patterns are markedly different for the conference and the museum case, and they are strongly impacted by the causal structure of the network data. A deeper study of the spreading paths shows that the mere knowledge of static aggregated networks would lead to erroneous conclusions about the transmission paths on the dynamical networks.

Subfields: Modeling and Simulation, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/3c8a0a4c1e1d44c3e409d60fe66700b3d9d61524

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Citations in the corpus

Scalable Temporal Latent Space Inference for Link Prediction in Dynamic Social Networks (2014, neutral): “Infection [34] 410 17,298 2,765 8 Facebook [35] 63,731 817,035 183,412 5 HepPh [36] 28,093 4,596,803 3,148,447 9 DBLP [37] 1,314,050 18,986,618 10,724,828 11 YouTube [38] 3,223,589 9,375,374 9,375,374 7 Fig.”
Evolution of Cooperation in Multiplex Networks (2012, background): “These advances has been largely facilitated by the explosion of data about mobile communication [28, 29], web-based social platforms [26, 27] and even the monitoring of face-to-face human interactions [30, 31].”
High-Resolution Measurements of Face-to-Face Contact Patterns in a Primary School (2011, background): “Some properties are however expected to be rather general, such as the heterogeneity of the contact durations and of the cumulated contact durations, that has been observed in several other settings [3,11,15,18,19] and seems to be ubiquitous in human interactions.”
TUDataset: A collection of benchmark datasets for learning with graphs (2020, methodology): “, 2009) as well as contacts between students at the MIT (Eagle & Pentland, 2006), in a HIGHSCHOOL and visitors at the INFECTIOUS exhibition (Isella et al., 2011).”
Relational Representation Learning for Dynamic (Knowledge) Graphs: A Survey (2019, background): “HYPERTEXT09: The HYPERTEXT09 dataset described in [107] is a proximity network of attendees at the ACM Hypertext 2009 conference.”
Contact Patterns among High School Students (2014, background): “[20,21,24], a museum [24], a primary school [22], in which a strong impact of the class and age structure on the contact patterns was evidentiated, and hospitals [23,25].”
Coupled disease–behavior dynamics on complex networks: A review (2015, neutral): “We refer to [164], from where this figure has been adapted, for further details.”
The fundamental advantages of temporal networks (2016, background): “…communication dataset collected by the SocioPatterns collaboration, capturing face-to-face conversations between the attendees of a conference [31]; an ecological network, capturing antenna-body interactions between ants [32]; a biological network capturing the temporal dynamics of…”
Activity driven modeling of time varying networks (2012, background): “…for the definition of interaction processes based on the actual measurement of the activity of the agents forming the system, a task now made possible by the availability of time-resolved, high-quality data on the instantaneous activity of millions of agents in a wide variety of networks [33–37].”