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On the duration of facetoface contacts
EPJ Data Science volume 13, Article number: 4 (2024)
Abstract
The analysis of social networks, in particular those describing facetoface interactions between individuals, is complex due to the intertwining of the topological and temporal aspects. We revisit here both, using public data recorded by the sociopatterns wearable sensors in some very different sociological environments, putting particular emphasis on the contact duration timelines. As well known, the distribution of the contact duration for all the interactions within a group is broad, with tails that resemble each other, but not precisely, in different contexts. By separating each interacting pair, we find that the fluctuations of the contact duration around the meaninteraction time follow however a very similar pattern. This common robust behavior is observed on 7 different datasets. It suggests that, although the set of persons we interact with and the meantime spent together, depend strongly on the environment, our tendency to allocate more or less time than usual with a given individual is invariant, i.e. governed by some rules that lie outside the social context. Additional data reveal the same fluctuations in a baboon population. This new metric, which we call the relation “contrast”, can be used to build and test agentbased models, or as an input for describing long duration contacts in epidemiological studies.
1 Introduction
Since the advent of the Internet, the quantity of digital data describing our behavior has inflated, offering to scientists an unprecedented opportunity to study human interactions in a more quantitative way. This opened the field of sociology to dataanalysis and from the hardscience community, came the tacit idea that several aspects of the complex human behavior can be modeled [1–6]. With the rapid development of mobile technologies (GPS, Bluetooth, cellphones) a lot of effort was first put in trying to capture the patterns of human mobility (for a review, see [7]). A more local picture of our everyday social interactions can be obtained using dedicated proximity sensors. Following a pioneering experiment that equipped conference participants with pocket switched devices [8, 9], the sociopatterns collaboration (www.sociopatterns.org) developed some wearable sensors that allow to register the complex patterns of facetoface interactions [10, 11]. The radiofrequency signal is only recorded if two individual are in front of each other for a duration of a least 20 s (which is the timing resolution). We note that, from a sociological point of view, a distance below 1.5 m covers the traditional private (\({<}50\text{ cm}\)), personal (<1.2 m) and social (\({<}3.5\text{ m}\)) zones. The goal is not only to analyze social interactions but also to understand how information (or a disease) spreads over a real dynamical network [12–15]. Those sensors were worn by volunteers in several workrelated environments: scientific conferences [10, 12, 13], a hospital ward [16], an office [15] and at school [17, 18]. As part of a UNICEF program, they were also used to characterize social exchanges in small villages in Kenya and Malawi [19, 20] and for ethological studies on baboons [21].
It has been known for a long time that the overall distribution of the duration of contacts in face to face interactions is “broad” [8] and presents some “similarities” when observed in different environments (see [22] for a short review).
However, those comparisons were performed on data taken in some similar sociological environments, which are typically occidental, educated and often with a scientific background (in conferences or highschool). Here we wish to extend the study of facetoface interactions by comparing them to some very different datasets that were originally designed for other aims. The fist one are the data taken in the rural Malawi village. The second one concerns interactions among baboons in a primatology center.
Moreover, there is more information in the data than what was previously presented [10, 11]. Indeed, one has access to the full timeline of interactions for each pair of individuals separately (what we call in the following a “relation”). This allows to study the meaninteraction time per relation and, most importantly, deviations of the contact duration from it, which reveals the underlying relation dynamics. We will show that they are surprisingly similar in all the settings.
After describing our data selection and methodological differences with some previous studies in Sect. 2, we will focus on the details of the temporal interactions in Sect. 3.2 after showing rapidly that social interactions among the participants are obviously very different in each environment. We will introduce the concept of contrast of the contact duration (deviation from the mean) and show that the distributions are extremely similar on each dataset and for each relation individually. In the Discussion part, we comment on the utility of using the robust contrast distribution in improving agentbased models, and conclude summarizing the results and highlighting some possible future extensions. Some extra information, referred to in the text, is given in the Suplementary Information (SI) document in Additional file 1.
2 Material and methods
2.1 Datasets
We have chosen four datasets from the sociopatterns web site, sociologically most dissimilar.

1.
hosp: these are early data collected over 3 days^{Footnote 1} on 75 participants in the geriatric unit of a hospital in Lyon (France) [16]. Most interactions (75%) involve nurses and patients.

2.
conf: these are also some early classical data from the ACM Hypertext 2009 (www.ht2009.org) conference that involved about a hundred of participants for 3 days [13] in Torino (Italy). The audience is international with a scientific background. There exist also some data taken at another conference in Nice in 2009 (SFHH, [23]) with more participants, but we prefer to use the former which has a number of individuals comparable to the other datasets. However we have checked that we obtain similar results with the SFHH data.

3.
malawi: these proximity data were taken in a small village of the district of Dowa in Malawi (Africa) where 86 participants agreed to participate for 13 (complete) days. Interestingly those data contain both extra and intrahousehold interactions, although we will not distinguish them here. This community consists essentially of farmers.

4.
baboons: those data were taken at a CNRS Primate Center near Marseille (France) where 13 baboons were equipped with the sensors for a duration of 26 days. The goal was to study their interactions, and study how conclusions reached from dataanalysis match those provided by human observation.
With that choice, we span very different sociological environments. We have also analyzed a few other datasets collected at the SFHH conference, an office and a highschool. They give similar results (results are shown in the SI) but we consider them as sociologically closer to the conf one. We have chosen to focus on the sociopatterns data since they provide a consistent set taken with the very same devices, minimizing possible sources of systematic errors.
2.2 Differences with previous studies
Previous studies considered the overall temporal properties of interactions, i.e. without differentiating the pair of people interacting. In this work we will put accent on the temporal properties of each pair separately.
Probability distribution functions (p.d.f) are often estimated by histograms, i.e. by counting the number of samples that fall within some bin. But for heavytailed distributions the size of the bins is delicate to choose. With a constant size binning, several bins end up empty for large values. Using a logarithmically increasing binning is neither a solution since it supposes that the distribution is constant on the wide range of last bins. Following [24], we will use instead the probability to exceed function (p.t.e, also known as the “complementary cumulative distribution function” or Zipf plot) which is computed simply by sorting the samples and plotting them with respect to their relative frequency. In this way, one does not need to define a binning and the distribution is easier to apprehend.
3 Results
3.1 Interactions between individuals
Since it is not our primary goal to study the social structures in those very different communities, we just highlight visually some differences on Fig. 1 which shows 24 hr timeaggregated graphs of the relations between individuals.
The graphs for the hosp and especially the conf datasets show a strongly connected core. The malawi one is much sparser, while the baboons one is almost complete showing that each animal interact with all the others.
Table 1 gives a more quantitative view of some of the graph’s properties. The number of different people met per day (the degree of the graphs) is about 20 in both the hospital and the conference environments. As is apparent in Fig. 1(c), it is much smaller in the rural community (3). But the interaction times are longer (≃25 min) which reflect different sector of activities (agrarian and including interhousing relations for the malawi data).
The strength of the relation represents the total time per individual spent interacting with others per day. It is essentially the product of the mean number of people met per day by the time spent interacting with them (\(\langle s\rangle \simeq \langle k\rangle \langle w\rangle \)). It varies by a factor of two (from 45 min to 1.5 h) although the large standarddeviations indicates important daily variations due to the heavytail of the distribution.
The comparison to the baboons dataset should be handled with care since there is a much smaller number of agents (13). Since each baboon interacts essentially with each other (Fig. 1(d)), the mean degree is bounded to \(\langle k\rangle \simeq N\). On the other hand, their small number possibly increases their interaction duration (\(\langle w\rangle \)) so that the strength of their relation is finally similar to that of the human groups.
The goal of this short section is not to dwell into the topological details of these time evolving graphs, but to illustrate that, as expected, these heterogeneous sociological groups show some very distinct interaction patterns between individuals.
3.2 Face to face temporal relations
We are interested in the duration of the contacts in those different networks. Figure 2 shows a classical distribution, that of the duration of contacts. We emphasize that such a representation mixes all the interactions of all the participants in the same plot. As well known, these distributions are “heavytailed”; most interactions are of short duration (at the minute level) but some may drift up to an hour. Interactions for people in malawi tend to last longer than for all the others. The baboons’ duration of interaction is similar to the human ones (as noticed in [21]), although there are some sizable differences at short times, somewhat squeezed by the logarithmic scale. Overall, although there is a common trend, some differences appear too.
The new aspect of this work concerns the detail of each relation separately. For a given datataking period, each relation consists in a set of intervals measuring the beginning and end times of the interaction at the resolution of the instruments (20 s). There is a varying number of interactions (intervals) per relation, that we call \(N_{\mathrm{int}}(r)\). In the following we will consider the duration of the interactions that we note \(\{t_{i}(r)\}_{i=1,\ldots ,N_{\mathrm{int}}(r)}\). They are thus variablesize timelines expressed in units of the resolution step.
The number of registered interactions for a given pair depends on the total duration of the experiments (Table 1) but we may compare them just for one day. The distribution of this variable is shown in Fig. 3(a). It is clearly different for each group. People at the conference tend to interact (with the same person) less often. In 65% of the cases it is only once per day, against 25% for the hosp and malawi datasets, and 3% for baboons.
The mean interaction time per relation
is shown in Fig. 3(b). Here again distributions are heavytailed and different. There is a marked difference between animals and humans, the former interacting for shorter times.
We are now interested in studying the deviations of the contact duration from the mean value for a given relation. Indeed, in physics the dynamics of a process is often revealed by such a quantity. For instance in cosmology, one uses the “density contrast” that represents the galactic density divided by its mean value. It is the fundamental quantity which traces the dynamics of the underlying field (see e.g. [26]). Inspired by this example, we propose to study what we call the ```duration contrast”, or simply “contrast” which is the simplest dimensionless quantity we can form to study deviations from the meanvalue
where r recalls that the quantity varies for each relation. The contrast represents our tendency to spend more or less time than usual with a given individual. Note that “usual” is meant as the meaninteraction time between the two peculiar agents (Fig. 3) and varies for each relation. For a small number of samples, the arithmetic mean (Eq. (1)) is however a poor estimate of the true meantime and also strongly correlated to the individual samples. Taking the ratio leads to a very noisy estimate of the true contrast variable. In the following we will then apply a cut to keep timelines with a sufficient number of samples. Since the distributions are very broad we require at least \(N_{\mathrm{int}}(r)>50\) contacts in a relation. We will study later the effect of this cut on the results. On the complete datasets, we are left with respectively 57, 26, 91 and 70 timelines for the hosp, conf, malawi and baboons datasets. We show the p.t.e distributions of the contact duration contrast for the 4 groups in Fig. 4.
The tails look now very similar up to 10 times the meantime. The same distribution is observed on data from another conference, an office and a highschool (SI Appendix, S2). Thus, a (very) similar distribution is observed on 7 independent datasets.
To be more quantitative and assess the level of compatibility between the distributions, we use a MonteCarlo method. For each dataset, we numerically invert the empirical distribution functions (which are one minus the p.t.e’s shown on Fig. 4) to construct the inverse cumulative function \(F^{1}\). We then draw N numbers u from a \([0,1]\) uniform distribution, transform them with \(F^{1}(u)\) and reconstruct the p.t.e. The procedure is repeated 100 times and all distributions are plotted on top of each other on Fig. 5.
One sees that the distributions are indeed all compatible in the \(0.6\lesssim \delta \lesssim 10\) range, where the upper bound comes from the limited sample size of the hosp and conf datasets, and the lower one from slight (but statistically significant) differences for low values. This will be our range of interest in the following.
Since the datataking periods are very heterogeneous (ranging from 3 days for the conf and hosp datasets, to 12 and 26 for the malawi and baboons ones respectively) we have split the data day by day and verified that no particular one(s) particularly affects the results (SI Appendix, S31). We have also removed randomly a fraction of the agents (up to 50%), i.e. we removed all relations involving those agents, which did not affect the contrast distributions in a sizable way (SI Appendix, S32. Both tests confirm the robustness of the result.
Another option for studying deviations from the mean is to use the zscore
where σ represents the standarddeviation of the duration values. The results obtained with this variable are very similar to the ones with the contrast (SI Appendix, S4) and we did not notice any difference on the tests that are presented later. Since the contrast variable is somewhat simpler (the zscore involving second order statistics) we only focus in the following on it.
We consider the impact of applying the \(N_{\mathrm{int}}(r)>50\) cut. First, we note that similar results are obtained with a lower cut value as \(N_{\mathrm{int}}>30\) (SI Appendix, S5). We then show that we can still reproduce the contrast distribution without any cut, using only the distributions with the cut (Fig. 4). To this purpose we perform MonteCarlo simulations. For a given dataset, for each relation (without any cut), we draw \(N_{\mathrm{int}}(r)\) random numbers following Fig. 4 distribution to obtain \(\delta _{i=1,\ldots ,N_{\mathrm{int}}}\) contrast values. Those samples are obtained from the distribution with the \(N_{\mathrm{int}}(r)>50\) cut, so with precise mean values that we call μ. We may mimic the statistical fluctuations due to any \(N_{\mathrm{int}}(r)\) value, by using the ratio
since μ actually cancels out. We compare the measured contrast distribution to the one observed on data, this time without any \(N_{\mathrm{int}}(r)\) cut, in Fig. 6 for the conf dataset. We reproduce correctly the whole contrast distribution using only the Fig. 4 one obtained with ≃1% of the data (\(N_{\mathrm{int}}>50\)). Similar results are obtained on the other datasets (SI Appendix, S6.1). This shows that the contrast distribution obtained from the large sample statistics is sufficient to reproduce any number of interactions, including smallsample ones. In other words, the \(N_{\mathrm{int}}(r)>50\) cut only cleans the data without affecting the underlying “true” contrast distribution.
To check that the contrast distribution is not artificially produced by the procedure of dividing the timelines by their mean value, we use the hosp dataset to retrieve the set of interacting agents and their corresponding characteristics \(N_{\mathrm{int}}(r)\) and \(\bar{t}(r)\). We then draw \(N_{\mathrm{int}}(r)\) random numbers following a Poisson distribution of parameter \(\bar{t}(r)\) and recompute the contrast. The result is shown in Fig. 7 which is clearly different from the results observed on the data.
The shape of the observed contrast distribution (Fig. 4) is nontrivial. It is neither of exponential nor of powerlaw form. A stretchedexponential form is neither satisfactory. Empirically, we could obtain a reasonable fit in the \(0.6 \lesssim \delta \lesssim 10\) region, by combining both a powerlaw and an exponential function
The denominator is here to enhance short contrasts, while the exponential term describes the long ones. This could be an indication of the existence of two regimes, one for short times when communications are more informative and a longer one when real conversations form [27].
At this point, we have shown that the combined contrast duration (i.e. for all relations) follows a very similar distribution. We now consider each relation separately and show in Fig. 8 a superposition of the contrast duration distributions with the \(N_{\mathrm{int}}(r)>50\) cut (similar results are observed without it but are, as expected, more noisy (see SI Appendix, S6.2).
They all follow rather closely the common contrast distribution. In other words, while the choice of individuals we meet (Fig. 1), the interaction rate (Fig. 3(a)) and meantime spent together (Fig. 3(b)) varies strongly with the environment, the propensity to spend more (or less) time than usual with a given individual, is remarkably similar. This points to the idea that once a facetoface contact is triggered it follows its own dynamics, out of the sociological context.
For the sake of completeness, we note that we found no sizable correlations between the contact duration within the timelines (see SI Appendix, S7). This indicates one can draw independent samples using Eq. (5).
We also considered the intercontact (or “gap”) time in the relations to see whether its contrast reveals features similar to the duration ones. This is not the case as shown in Fig. 9. The contrast of the intercontact time thus seems to be more dependent on the sociological context.
4 Comparison with a model
The contrast distribution can be used as a new metric when studying facetoface temporal graphs in order to test and improve existing agentbased models designed to reproduce the full evolution of a set of individuals. For instance, the “force directed motion” (FDM) model is successful in describing several key features of observed facetoface interactions [6]. Based on the idea of attractiveness between some agents performing a randomwalk within a bounded perimeter [4, 28], the model further includes the concept of “similarity” between two individuals [29], known as homophily in social sciences. The similarity \(s_{ij}\) influences the time two agents spend together and the way the randomwalk is biased. The model assumes that the contact duration between two agents is exponentially distributed with a rate \(s_{ij}/\mu _{1}\), where \(\mu _{1}\) is adjusted on the data to reproduce the overall duration of contacts. We have run the code provided by the authors with their setup corresponding to the hosp dataset, to test the distribution of the contrast variable. Figure 10 shows that the model distribution falls too steeply. We have tried adapting the parameters and some parts of the code but could not find a configuration giving a better contrast distribution (see SI Appendix, S8) .^{Footnote 2}
Modeling correctly the tails of the contact duration is also essential in epidemiological studies since the spread of a disease happens mostly during long interactions. For a given meaninteraction time, Eq. (5) allows to simulate a much more realistic duration of contacts than a Poissonnian one. This can be used in SIRlike statistical inference, or using agentbased models, for the precise modeling of long interactions.
5 Conclusion
We have compared facetoface interaction data taken in some very different environments; some were recorded in a European hospital and during a scientific conference, others in a small village in Africa. With the original intention to pinpoint differences with the results concerning humans, we have also included data on baboons’ interactions in an enclosure.
Although the topological structures (who interacts with whom) and the meantime spent together are clearly dependent on the sociological environment, it appears that the deviations from the meantime for each pair (do we spend more/less time than usual with a given person) follow a very similar distribution, including for baboons. We (and baboons) tend to interact most often for much less time than “usual” with a given individual and sometimes, but rarely, much longer. What is striking is that the distribution for this quantity, which we call the “relation contrast” looks universal. It is the same for people at a scientific conference or farmers in a small Malawi village (and baboons in an enclosure), see Fig. 4 (also SI Appendix, S2 for the 7 datasets).
These results suggests that, once a facetoface contact is triggered, it follows its own dynamics independently from the social context. This is maybe not a big surprise to a sociologist in particular working in the field of Conversation Analysis [27] where it is postulated that each conversation follows some rules independently from the social context .^{Footnote 3} But to our knowledge, this was not noticed by physicists and may help disentangling the topological and temporal aspects of facetoface interactions.
The possible universality of the relation contrast must be challenged with more data. On the animal side, one should consider groups of animals with strong social interactions, that can be identified (labeled) and followed individually. Hominids, as baboons, are known to have social behaviors close to ours, which probably explains the similarity of the contrast distribution with the human’s one. Chimpanzee or bonobo’s data should show similar characteristic. Concerning mammals, we could think of tracking individuals in elephant herds or wolf packs but it’s difficult to acquire precise data in the wild. The most promising approach concerns the study of social insect networks [31]. Details about ant interactions is probably the most feasible since recent techniques allow to tag and follow each individual separately [32]. On the human side, we need to check whether the contrast is influenced by age. Since children perceive time differently from adults, following the contact patterns of young children in a nursery could provide a valuable insight into this question.
Data availability
• The datasets analyzed during the current study are available in the sociopatterns repository, www.sociopatterns.org
• The FDM code was downloaded on 10 June 2023 from https://bitbucket.org/mrodrflr/similarity_forces
• The python3 software used to produce the results is available from https://gitlab.in2p3.fr/plaszczy/coll
• The graphrelated computations and Fig. 1 were obtained with the graphtool (v 2.43) software [33].
Notes
here and in the following, we will only consider complete (24 h) day periods.
The fact that similar results are observed on baboons requires however to enlarge the concept of “discussion”.
Abbreviations
 p.t.e:

probability to exceed
 SI :

Supplementary Information
 hosp :

hospital dataset
 conf :

conference dataset
 malawi :

Malawi dataset
 baboons :

baboons dataset
References
Song C, Koren T, Wang P, Barabási AL (2010) Modelling the scaling properties of human mobility. Nat Phys 6(10):818–823. https://doi.org/10.1038/nphys1760
Stehle J, Barrat A, Bianconi G (2010) Dynamical and bursty interactions in social networks. Phys Rev E 81(3):035101. https://doi.org/10.1103/PhysRevE.81.035101. arXiv:1002.4109
Zhao K, Stehle J, Bianconi G, Barrat A (2011) Social network dynamics of facetoface interactions. Phys Rev E 83(5):056109. https://doi.org/10.1103/PhysRevE.83.056109. arXiv:1102.2423
Starnini M, Baronchelli A, PastorSatorras R (2013) Modeling human dynamics of facetoface interaction networks. Phys Rev Lett 110(16):168701. https://doi.org/10.1103/PhysRevLett.110.168701
Sekara V, Stopczynski A, Lehmann S (2016) Fundamental structures of dynamic social networks. Proc Natl Acad Sci 113(36):9977–9982. https://doi.org/10.1073/pnas.1602803113. arXiv:1506.04704
Flores MAR, Papadopoulos F (2018) Similarity forces and recurrent components in human facetoface interaction networks. Phys Rev Lett 121(25):258301. https://doi.org/10.1103/PhysRevLett.121.258301
Barbosa H, Barthelemy M, Ghoshal G, James CR, Lenormand M, Louail T, Menezes R, Ramasco JJ, Simini F, Tomasini M (2018) Human mobility: models and applications. Phys Rep 734:1–74. https://doi.org/10.1016/j.physrep.2018.01.001
Hui P, Chaintreau A, Scott J, Gass R, Crowcroft J, Diot C (2005) Pocket switched networks and human mobility in conference environments. In: Proceeding of the 2005 ACM SIGCOMM workshop on delaytolerant networking – WDTN ’05. ACM, Philadelphia, pp 244–251. https://doi.org/10.1145/1080139.1080142
Scherrer A, Borgnat P, Fleury E, Guillaume JL, Robardet C (2008) Description and simulation of dynamic mobility networks. Comput Netw 52(15):2842–2858. https://doi.org/10.1016/j.comnet.2008.06.007
Cattuto C, Van den Broeck W, Barrat A, Colizza V, Pinton JF, Vespignani A (2010) Dynamics of persontoperson interactions from distributed RFID sensor networks. PLoS ONE 5(7):11596. https://doi.org/10.1371/journal.pone.0011596
Barrat A, Cattuto C, Tozzi AE, Vanhems P, Voirin N (2014) Measuring contact patterns with wearable sensors: methods, data characteristics and applications to datadriven simulations of infectious diseases. Clin Microbiol Infect 20(1):10–16. https://doi.org/10.1111/14690691.12472
Stehlé J, Voirin N, Barrat A, Cattuto C, Colizza V, Isella L, Régis C, Pinton JF, Khanafer N, Van den Broeck W, Vanhems P (2011) Simulation of an SEIR infectious disease model on the dynamic contact network of conference attendees. BMC Med 9(1):87. https://doi.org/10.1186/17417015987
Isella L, Stehlé J, Barrat A, Cattuto C, Pinton JF, Van den Broeck W (2010) What’s in a crowd? Analysis of facetoface behavioral networks. J Theor Biol 271(1):166–180. https://doi.org/10.1016/j.jtbi.2010.11.033
Starnini M, Baronchelli A, Barrat A, PastorSatorras R (2012) Random walks on temporal networks. Phys Rev E 85(5):056115. https://doi.org/10.1103/PhysRevE.85.056115
Génois M, Vestergaard CL, Fournet J, Panisson A, Bonmarin I, Barrat A (2015) Data on facetoface contacts in an office building suggest a lowcost vaccination strategy based on community linkers. Netw Sci 3(3):326–347. https://doi.org/10.1017/nws.2015.10
Vanhems P, Barrat A, Cattuto C, Pinton JF, Khanafer N, Régis C, Kim BA, Comte B (2013) Estimating potential infection transmission routes in hospital wards using wearable proximity sensors. PLoS ONE 8(9):73970. https://doi.org/10.1371/journal.pone.0073970
Fournet J, Barrat A (2014) Contact patterns among high school students. PLoS ONE 9(9):107878. https://doi.org/10.1371/journal.pone.0107878
Mastrandrea R, Fournet J, Barrat A (2015) Contact patterns in a high school: a comparison between data collected using wearable sensors, contact diaries and friendship surveys. PLoS ONE 10(9):0136497. https://doi.org/10.1371/journal.pone.0136497
Kiti MC, Tizzoni M, Kinyanjui TM, Koech DC, Munywoki PK, Meriac M, Cappa L, Panisson A, Barrat A, Cattuto C, Nokes DJ (2016) Quantifying social contacts in a household setting of rural Kenya using wearable proximity sensors. EPJ Data Sci 5(1):21. https://doi.org/10.1140/epjds/s1368801600842
Ozella L, Paolotti D, Lichand G, Rodríguez JP, Haenni S, Phuka J, LealNeto OB, Cattuto C (2021) Using wearable proximity sensors to characterize social contact patterns in a village of rural Malawi. EPJ Data Sci 10(1):46. https://doi.org/10.1140/epjds/s1368802100302w
Gelardi V, Godard J, Paleressompoulle D, Claidiere N, Barrat A (2020) Measuring social networks in primates: wearable sensors versus direct observations. Proc R Soc A, Math Phys Eng Sci 476(2236):20190737. https://doi.org/10.1098/rspa.2019.0737
Barrat A, Cattuto C (2015) Facetoface interactions. In: Gonçalves B, Perra N (eds) Social phenomena. Springer, Cham, pp 37–57. https://doi.org/10.1007/9783319140117_3
Génois M, Barrat A (2018) Can colocation be used as a proxy for facetoface contacts? EPJ Data Sci 7(1):11. https://doi.org/10.1140/epjds/s1368801801401
Newman MEJ (2005) Power laws, Pareto distributions and Zipf’s law. Contemp Phys 46(5):323–351. https://doi.org/10.1080/00107510500052444. arXiv:condmat/0412004
Barrat A, Barthélemy M, PastorSatorras R, Vespignani A (2004) The architecture of complex weighted networks. Proc Natl Acad Sci 101(11):3747–3752. https://doi.org/10.1073/pnas.0400087101
Peebles PJE (1980) III.62. The largescale structure of the universe. Princeton University Press, Princeton
Button G, Lynch M, Sharrock W (2022) Ethnomethodology, conversation analysis and constructive analysis: on formal structures of practical action, 1st edn. Routledge, London. https://doi.org/10.4324/9781003220794
Starnini M, Baronchelli A, PastorSatorras R (2016) Model reproduces individual, group and collective dynamics of human contact networks. Soc Netw 47:130–137. https://doi.org/10.1016/j.socnet.2016.06.002
Papadopoulos F, Kitsak M, Serrano MÁ, Boguñá M, Krioukov D (2012) Popularity versus similarity in growing networks. Nature 489(7417):537–540. https://doi.org/10.1038/nature11459
Krioukov D, Papadopoulos F, Kitsak M, Vahdat A, Boguñá M (2010) Hyperbolic geometry of complex networks. Phys Rev E 82(3):036106. https://doi.org/10.1103/PhysRevE.82.036106
Fewell JH (2003) Social insect networks. Science 301(5641):1867–1870. https://doi.org/10.1126/science.1088945
Greenwald E, Segre E, Feinerman O (2015) Ant trophallactic networks: simultaneous measurement of interaction patterns and food dissemination. Sci Rep 5(1):12496. https://doi.org/10.1038/srep12496
Peixoto TP (2014) The graphtool Python library. figshare. https://doi.org/10.6084/m9.figshare.1164194. Accessed 20140910
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Plaszczynski, S., Nakamura, G., Grammaticos, B. et al. On the duration of facetoface contacts. EPJ Data Sci. 13, 4 (2024). https://doi.org/10.1140/epjds/s1368802300444z
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DOI: https://doi.org/10.1140/epjds/s1368802300444z