### 2.1 Screen-on events can be used for chronotype assessment

We use time-stamped data on “screen-on” events from the smartphone data-collection apps to assign a behavioral chronotype to each participant. Whenever the participant uses the smartphone, from making a call to checking the time, the phone’s screen is turned on, and the data-collection app records the time of this event. We use the frequency of these events as a statistical proxy for the daily activity rhythm of the participant, since frequent screen-on events tell that the participant is awake, and night-time event frequencies are typically low or zero. To form an overview of the daily activity patterns of participants, we aggregate the screen-on event frequencies in hourly time bins for the four weekdays from Monday to Thursday for each of the \(N=400\) participants who used their phones actively during the whole observation period (see Methods for details; note that for network reconstruction more participants were used). A population-level average rhythm is computed for reference.

Figure 1 shows the screen-on daily rhythms of two study participants (upper and lower panels), together with the population average. The phase of the daily pattern of the student in panel (A) is consistently shifted towards the night, while the student in panel (B) displays a pattern whose phase is shifted towards morning. These phase shifts are captured by the event frequencies in the early morning hours (5 AM to 7 AM) and late hours of the day (midnight to 2 AM); “larks” are associated with above-average morning activity and below-average night-time activity, and the opposite holds for “owls” (see Methods for details). On this basis, 20% of the participants (\(N=80\)) are labeled as larks, 20% as owls (\(N=80\)), and the rest as intermediate (\(N=240\)). These percentages have been chosen to match the literature [11, 29] (see Methods). For robustness tests with smaller/larger percentages, see Sect. 2.4. For an illustration of the criteria, see panel (A) in Fig. 2.

In recent years, many studies have tried to unobtrusively measure sleep patterns of individuals by using data collected passively and without any active engagement of the user with the device for the purpose of the study [30–32]. These studies have mostly focused on estimating an individual’s different sleep parameters—e.g., sleep duration, mid-sleep time—for each night of sleep during the collection period. Because our focus has been on determining each subject’s chronotype, a characteristic that does not change frequently, we have instead used data aggregated over a longer period of time. This makes our results less sensitive to random behavioral variations.

### 2.2 Owls have larger personal networks than larks

We first construct the personal networks of all participants based on both call and text data. For this we use each (hashed and anonymized) phone number that the participant communicates with (through calls and text messages) as a proxy of a social relationship. In this network, each individual is a node and communication events (calls and text messages) between people are the links. The degree of a node (the personal network size) is the total number of people in contact with that node, while the strength of a link is the number of times that it is activated (*i.e.*, the total number of interactions between two nodes). When constructing the personal network of each individual, we consider communication with any phone number, not only those associated with other study participants (see Methods). In addition to personal network membership, we count the total number of calls and texts with each contact, as well as the average outgoing call duration. We then study the properties of the personal networks of individuals of each chronotype separately for which we use outgoing calls and text messages.

The average personal network sizes for students of each chronotype are shown in Fig. 2(B). It is evident that owls have personal networks that are much larger than those of larks, with the intermediate chronotype positioned in between (owls: network size \(k=70.7 \pm4.1\), larks: \(k=51.0 \pm2.7\), intermediate \(k=55.4 \pm1.9\)). When the average call durations and total frequencies of calls and texts per social contact are considered, an opposite trend becomes visible (Fig. 2(C) and (D)): owls make the shortest calls on average and their communication frequency per social tie is the lowest as compared to the intermediate chronotype and in particular to larks. More detailed analysis indicates that owls’ calls are on average shorter than those of other chronotypes at all times of day except at night where the differences are within standard errors (mornings owls/larks: 83/100; afternoons owls/larks 86/103; evenings owls/larks 106/141; nights owls/larks 68/64; all numbers in seconds). This reflects the known sub-linear scaling between node degree and strength in social networks (see, e.g., [33]); the larger the number of relationships, the less time is available for each of them. There are also differences in the numbers of screen-on events for the different chronotypes: the mean of number of screen-on events in weeks 2–51 is 19,829 for larks, 23,509 for members of the intermediate chronotype, and 25,140 for owls.

### 2.3 Owls are more central than larks in the social network of participants

In order to study the network centrality of each participant, we constructed the social network of participating students, so that two individuals *i* and *j* are connected with an unweighted link if there are either calls or text messages from *i* to *j* and from *j* to *i* (see Methods for details). This network consists of \(N=734\) participants; out of these, 366 had enough screen-on events to be assigned a chronotype (for filtering criteria, see Methods). We then computed the values of various network centrality measures for all individuals within each of the three chronotypes. The chosen measures were (i) betweenness centrality, measuring the number of shortest paths through a network node, (ii) closeness centrality, quantifying the inverted average geodesic distance to other nodes, (iii) eigenvector centrality, reflecting the level of connectivity to high-centrality nodes in an iterative fashion, and (iv) core number, indicating membership in a core where all nodes are linked to other member nodes with at least *k* links.

These four centrality measures are displayed in Fig. 3 (panels (A)–(D)), together with a visualization of the network (panel (E)). There is an increasing trend in centrality from larks to owls for all centrality measures: owls are much more central than larks in the network. This is also reflected in the network visualization: owls (blue) are more frequently located in central parts of the network than larks (red).

To test whether the centralities of owls and larks differ only because their degrees differ, we used the so-called configuration model (see Sect. 4.6). It randomizes the structure of the network while retaining the degrees and chronotypes of nodes. We applied the configuration model as the null model and tested whether the ratios of centralities of owls to larks are the same in the null model as observed in the real network. We found that the higher degrees of owls do not alone explain their higher centralities (Fig. 4). For all centrality measures, the real owl-lark ratios lie 5 to 12 standard deviations away from the null model mean. As an example, in the real data, the average eigenvector centrality of owls is twice higher than that of larks. In the reference model, however, the corresponding factor is only 1.07 on average. This means that there is a small effect from the higher degrees of owls (the ratio is 1.07 instead of 1), but it cannot explain the observed factor of two. To summarize, in the \(N=10^{4}\) runs of the configuration model, we never observed owl-to-lark centrality ratios as high as in the original network, for any centrality measure. Therefore, the null hypothesis can be rejected: degrees are not enough to explain why the centralities of chronotypes differ.

### 2.4 Robustness of results

To test how robust our conclusions are against variations in the analysis pipeline, we have recomputed the results with different parameterizations. In particular, we have determined the students’ chronotypes (1) using data from a shorter time range (half a year instead of the whole year), (2) from screen-on event frequencies from Friday to Sunday instead of Monday to Thursday, (3) with stricter filtering criteria, taking into account only students who have at least 280 screen-on and screen-off events in each week of the study (\(N=222\)), and (4) using smaller and larger percentages of larks and owls (15% and 25% instead of the 20% on which the results so far have been based).

The outcome of the above has been that while there are minor variations in the exact numbers, our results qualitatively hold for all cases (1) to (4). The results for centrality measures are shown in Fig. 5.

We have also tested the behavior of eigenvector centrality using edge weights and separately constructed networks from calls and text messages, with the logarithms of their numbers as weights. Eigenvector centrality is suited for working with weights (unlike the other three). The rationale behind keeping the networks separate is that the numbers of calls and texts measure different things: one conversation may take one call but a large number of text messages. The rationale behind taking the logarithm is that the distributions of these numbers are very broad and without the log, the highest-weight link dominates the entire measure. However, with log weights, in terms of eigenvector centrality, owls are most central and larks least central in both call and text-message networks.

Finally, we have also tested our main results with a method based on Non-negative Matrix Factorization (NMF) for chronotype identification (T.A., S.L., J.S., manuscript in preparation); our results hold when using this method as well.