arXiv subjects

Petter Holme

Publications and source records attributed to Petter Holme.

At least 55 records · Page 3Linked to original sources

arXiv2021

Autopoietic influence hierarchies in pancreatic $β$-cells

$β$-cells are biologically essential for humans and other vertebrates. Because their functionality arises from cell-cell interactions, they are also a model system for collective organization among cells. There are currently two contradictory pictures of this organization: the hub-cell idea pointing at leaders who coordinate the others, and the electrophysiological theory describing all cells as equal. We use new data and computational modeling to reconcile these pictures. We find via a network representation of interacting $β$-cells that leaders emerge naturally (confirming the hub-cell idea), yet all cells can take the hub role following a perturbation (in line with electrophysiology). A useful network representation of complex systems is via pairwise cross-correlations between a system's constituents. Recently, attempts have been made at representing pancreatic $β$-cells collectives as networks, giving birth to the concept of hub cells, which contradicts established electrophysiological theory. Here, we reconcile the hub-cell idea with electrophysiology using empirical and computational methods. While the former reveals the functional importance of hubs, the latter show that hubs are an emergent property of communication among identical peers. Thus, individual cells are perfectly capable of playing the hub role, but in line with electrophysiology, they do not have to be genetically predisposed to do so. This points to an ultra-robust architecture of $β$-cell collectives and underpins some of their vital features.

physics.bio-ph↗

arXiv2020

Exit rights open complex pathways to cooperation

We study the evolutionary dynamics of the prisoner's dilemma game in which cooperators and defectors interact with another actor type called exiters. Rather than being exploited by defectors, exiters exit the game in favour of a small payoff. We find that this simple extension of the game allows cooperation to flourish in well-mixed populations when iterations or reputation are added. In networked populations, however, the exit option is less conducive to cooperation. Instead, it enables the coexistence of cooperators, defectors, and exiters through cyclic dominance. Other outcomes are also possible as the exit payoff increases or the network structure changes, including network-wide oscillations in actor abundances that may cause the extinction of exiters and the domination of defectors, although game parameters should favour exiting. The complex dynamics that emerges in the wake of a simple option to exit the game implies that nuances matter even if our analyses are restricted to incentives for rational behaviour.

q-bio.PE↗

arXiv2020

Building surrogate temporal network data from observed backbones

In many data sets, crucial elements co-exist with non-essential ones and noise. For data represented as networks in particular, several methods have been proposed to extract a "network backbone", i.e., the set of most important links. However, the question of how the resulting compressed views of the data can effectively be used has not been tackled. Here we address this issue by putting forward and exploring several systematic procedures to build surrogate data from various kinds of temporal network backbones. In particular, we explore how much information about the original data need to be retained alongside the backbone so that the surrogate data can be used in data-driven numerical simulations of spreading processes. We illustrate our results using empirical temporal networks with a broad variety of structures and properties.

physics.soc-ph↗

arXiv2020

Flexible imitation suppresses epidemics through better vaccination

The decision of whether or not to vaccinate is a complex one. It involves the contribution both to a social good -- herd immunity -- and to one's own well being. It is informed by social influence, personal experience, education, and mass media. In our work, we investigate a situation in which individuals make their choice based on how social neighbourhood responded to previous epidemics. We do this by proposing a minimalistic model using components from game theory, network theory and the modelling of epidemic spreading, and opinion dynamics. Individuals can use the information about the neighbourhood in two ways -- either they follow the majority or the best-performing neighbour. Furthermore, we let individuals learn which of these two decision-making strategies to follow from their experience. Our results show that the flexibility of individuals to chose how to integrate information from the neighbourhood increases the vaccine uptake and decreases the epidemic severity if the following conditions are fulfilled. First, the initial fraction of individuals who imitate the neighbourhood majority should be limited, and second, the memory of previous outbreaks should be sufficiently long. These results have implications for the acceptance of novel vaccines and raising awareness about vaccination, while also pointing to promising future research directions.

q-bio.PE↗

arXiv2020

Fast and principled simulations of the SIR model on temporal networks

The Susceptible-Infectious-Recovered (SIR) model is the canonical model of epidemics of infections that make people immune upon recovery. Many of the open questions in computational epidemiology concern the underlying contact structure's impact on models like the SIR model. Temporal networks constitute a theoretical framework capable of encoding structures both in the networks of who could infect whom and when these contacts happen. In this article, we discuss the detailed assumptions behind such simulations -- how to make them comparable with analytically tractable formulations of the SIR model, and at the same time, as realistic as possible. We also present a highly optimized, open-source code for this purpose and discuss all steps needed to make the program as fast as possible.

physics.soc-ph↗

arXiv2020

SI-spreading-based network embedding in static and temporal networks

Link prediction can be used to extract missing information, identify spurious interactions as well as forecast network evolution. Network embedding is a methodology to assign coordinates to nodes in a low dimensional vector space. By embedding nodes into vectors, the link prediction problem can be converted into a similarity comparison task. Nodes with similar embedding vectors are more likely to be connected. Classic network embedding algorithms are random-walk-based. They sample trajectory paths via random walks and generate node pairs from the trajectory paths. The node pair set is further used as the input for a Skip-Gram model, a representative language model that embeds nodes (which are regarded as words) into vectors. In the present study, we propose to replace random walk processes by a spreading process, namely the susceptible-infected (SI) model, to sample paths. Specifically, we propose two SI-spreading-based algorithms, SINE and TSINE, to embed static and temporal networks, respectively. The performance of our algorithms is evaluated by the missing link prediction task in comparison with state-of-the-art static and temporal network embedding algorithms. Results show that SINE and TSINE outperform the baselines across all six empirical datasets. We further find that the performance of SINE is mostly better than TSINE, suggesting that temporal information does not necessarily improve the embedding for missing link prediction. Moreover, we study the effect of the sampling size, quantified as the total length of the trajectory paths, on the performance of the embedding algorithms. The better performance of SINE and TSINE requires a smaller sampling size in comparison with the baseline algorithms. Hence, SI-spreading-based embedding tends to be more applicable to large-scale networks.

cs.SI↗

arXiv2020

Small inter-event times govern epidemic spreading on temporal networks

Just like the degrees of human and animal interaction networks, the distribution of the times between interactions is known to often be right-skewed and fat-tailed. Both these distributions affect epidemic dynamics strongly, but, as we show in this Letter, for very different reasons. Whereas the high degrees of the tail are critical for facilitating epidemics, it is the small interevent times that control the dynamics of epidemics. We investigate this effect both analytically and numerically for different versions of the Susceptible-Infected-Recovered model on different types of networks.

q-bio.PE↗

arXiv2020

Beyond ranking nodes: Predicting epidemic outbreak sizes by network centralities

Identifying important nodes for disease spreading is a central topic in network epidemiology. We investigate how well the position of a node, characterized by standard network measures, can predict its epidemiological importance in any graph of a given number of nodes. This is in contrast to other studies that deal with the easier prediction problem of ranking nodes by their epidemic importance in given graphs. As a benchmark for epidemic importance, we calculate the exact expected outbreak size given a node as the source. We study exhaustively all graphs of a given size, so do not restrict ourselves to certain generative models for graphs, nor to graph data sets. Due to the large number of possible nonisomorphic graphs of a fixed size, we are limited to 10-node graphs. We find that combinations of two or more centralities are predictive ($R^2$ scores of 0.91 or higher) even for the most difficult parameter values of the epidemic simulation. Typically, these successful combinations include one normalized spectral centralities (such as PageRank or Katz centrality) and one measure that is sensitive to the number of edges in the graph.

q-bio.PE↗

arXiv2020

The free and freer XY models

We study two versions of the XY model where the spins but also the interaction topology is allowed to change. In the free XY model, the number of links is fixed, but their positions in the network are not. We also study a more relaxed version where even the number of links is allowed to vary, we call it the freer XY model. When the interaction networks are dense enough, both models have phase transitions visible both in spin configurations and the network structure. The low-temperature phase in the free XY model, is characterized by tightly connected clusters of spins pointing in the same direction, and isolated spins disconnected from the rest. For the freer XY model the low-temperature phase is almost completely connected. In both models, exponents describing the magnetic ordering are mostly consistent with values of the mean-field theory of the standard XY model.

physics.comp-ph↗

arXiv2019

Coupling the circadian rhythms of population movement and the immune system in infectious disease modeling

The dynamics of infectious diseases propagating in populations depends both on human interaction patterns, the contagion process and the pathogenesis within hosts. The immune system follows a circadian rhythm and, consequently, the chance of getting infected varies with the time of day an individual is exposed to the pathogen. The movement and interaction of people also follow 24-hour cycles, which couples these two phenomena. We use a stochastic metapopulation model informed by hourly mobility data for two medium-sized Chinese cities. By this setup, we investigate how the epidemic risk depends on the difference of the clocks governing the population movement and the immune systems. In most of the scenarios we test, we observe circadian rhythms would constrain the pace and extent of disease emergence. The three measures (strength, outward transmission risk and introduction risk) are highly correlated with each other. For example of the Yushu City, outward transmission risk and introduction risk are correlated with a Pearson's correlation coefficient of 0.83, and the risks correlate to strength with coefficients of $-0.85$ and $-0.75$, respectively (all have $p<0.05$), in simulations with no circadian effect and $R_0=1.5$. The relation between the circadian rhythms of the immune system and daily routines in human mobility can affect the pace and extent of infectious disease spreading. Shifting commuting times could mitigate the emergence of outbreaks.

q-bio.PE↗

arXiv2019

Beyond the Coverage of Information Spreading: Analytical and Empirical Evidence of Re-exposure in Large-scale Online Social Networks

Peer influence and social contagion are key denominators in the adoption and participation of information spreading, such as news propagation, word-of-mouth or viral marketing. In this study, we argue that it is biased to only focus on the scale and coverage of information spreading, and propose that the level of influence reinforcement, quantified by the re-exposure rate, i.e., the rate of individuals who are repeatedly exposed to the same information, should be considered together to measure the effectiveness of spreading. We show that local network structural characteristics significantly affects the probability of being exposed or re-exposed to the same information. After analyzing trending news on the super large-scale online network of Sina Weibo (China's Twitter) with 430 million connected users, we find a class of users with extremely low exposure rate, even they are following tens of thousands of others; and the re-exposure rate is substantially higher for news with more transmission waves and stronger secondary forwarding. While exposure and re-exposure rate typically grow together with the scale of spreading, we find exceptional cases where it is possible to achieve a high exposure rate while maintaining low re-exposure rate, or vice versa.

physics.soc-ph↗

arXiv2019

Impact of perception models on friendship paradox and opinion formation

Topological heterogeneities of social networks have a strong impact on the individuals embedded in those networks. One of the interesting phenomena driven by such heterogeneities is the friendship paradox (FP), stating that the mean degree of one's neighbors is larger than the degree of oneself. Alternatively, one can use the median degree of neighbors as well as the fraction of neighbors having a higher degree than oneself. Each of these reflects on how people perceive their neighborhoods, i.e., their perception models, hence how they feel peer pressure. In our paper, we study the impact of perception models on the FP by comparing three versions of the perception model in networks generated with a given degree distribution and a tunable degree-degree correlation or assortativity. The increasing assortativity is expected to decrease network-level peer pressure, while we find a nontrivial behavior only for the mean-based perception model. By simulating opinion formation, in which the opinion adoption probability of an individual is given as a function of individual peer pressure, we find that it takes the longest time to reach consensus when individuals adopt the median-based perception model, compared to other versions. Our findings suggest that one needs to consider the proper perception model for better modeling human behaviors and social dynamics.

physics.soc-ph↗

arXiv2019

Detecting sequences of system states in temporal networks

Many time-evolving systems in nature, society and technology leave traces of the interactions within them. These interactions form temporal networks that reflect the states of the systems. In this work, we pursue a coarse-grained description of these systems by proposing a method to assign discrete states to the systems and inferring the sequence of such states from the data. Such states could, for example, correspond to a mental state (as inferred from neuroimaging data) or the operational state of an organization (as inferred by interpersonal communication). Our method combines a graph distance measure and hierarchical clustering. Using several empirical data sets of social temporal networks, we show that our method is capable of inferring the system's states such as distinct activities in a school and a weekday state as opposed to a weekend state. We expect the methods to be equally useful in other settings such as temporally varying protein interactions, ecological interspecific interactions, functional connectivity in the brain and adaptive social networks.

cs.SI↗

arXiv2018

Urban contact structures for epidemic simulations: Correcting biases in data-driven approaches

Epidemics are emergent phenomena depending on the epidemiological characteristics of pathogens and the interaction and movement of people. Public transit systems have provided much important information about the movement of people, but there are also other means of transportation (e.g., bicycle and private car), that are invisible to public transit data. This discrepancy can induce a bias in disease models that leads to mispredictions of epidemic growth (e.g., peak prevalence and time). In our study, we aim to advance and compare the epidemic spreading dynamics using public transit trips, in contrast to more accurate estimates of population movement using mobile phones traces. In our study, we simulate epidemic outbreaks in a cohort of two million mobile phone users. We use a metapopulation model incorporating susceptible-infected-recovered dynamics to analyze and compare different effective contract matrices, constructed by the public transit systems and mobile phones respectively, on the process of epidemics. We find that epidemic outbreaks using public transit trips tend to be underestimated in terms of the epidemic spreading dynamics, reaching epidemic peaks weaker and later. This is rooted in a later introduction of new infectious people into uninfected locations.

cs.SI↗

arXiv2018

Epidemic extinction in networks: Insights from the 12,110 smallest graphs

We investigate the expected time to extinction in the susceptible-infectious-susceptible (SIS) model of disease spreading. Rather than using stochastic simulations, or asymptotic calculations in network models, we solve the extinction time exactly for all connected graphs with three to eight vertices. This approach enables us to discover descriptive relations that would be impossible with stochastic simulations. It also helps us discovering graphs and configurations of S and I with anomalous behaviors with respect to disease spreading. We find that for large transmission rates the extinction time is independent of the configurations, just dependent on the graph. In this limit, the number of vertices and edges determine the extinction time very accurately (deviations primarily coming from the fluctuations in degrees). We find that the rankings of configurations with respect to extinction times at low and high transmission rates are correlated at low prevalences and negatively correlated for high prevalences. The most important structural factor determining this ranking is the degrees of the infectious vertices.

q-bio.PE↗

arXiv2018

Periodicity in Movement Patterns Shapes Epidemic Risk in Urban Environments

Daily variation in human mobility modulates the speed and severity of emerging outbreaks, yet most epidemiological studies assume static contact patterns. With a highly mobile population exceeding 24 million people, Shanghai, China is a transportation hub at high risk for the importation and subsequent global propagation of infectious diseases. Here, we use a dynamic metapopulation model informed by hourly transit data for Shanghai to estimate epidemic risks across thousands of outbreak scenarios. We find that the rate of initial epidemic growth varies by more than twenty-fold, depending on the hour and neighborhood of disease introduction. The riskiest introductions are those occurring close to the city center and on Fridays--which bridge weekday and weekend transit patterns and thereby connect otherwise disconnected portions of the population. The identification of these spatio-temporal hotspots can inform more efficient targets for sentinel surveillance and strategies for mitigating transmission.

cs.SI↗

arXiv2018

Objective measures for sentinel surveillance in network epidemiology

Assume one has the capability of determining whether a node in a network is infectious or not by probing them. Then problem of optimizing sentinel surveillance in networks is to identify the nodes to probe such that an emerging disease outbreak can be discovered early or reliably. Whether the emphasis should be on early or reliable detection depends on the scenario in question. We investigate three objective measures from the literature quantifying the performance of nodes in sentinel surveillance -- the time to detection or extinction, the time to detection, and the frequency of detection. As a basis for the comparison, we use the susceptible-infectious-recovered model on static and temporal networks of human contacts. We show that, for some regions of parameter space, the three objective measures can rank the nodes very differently. This means sentinel surveillance is a class of problems, and solutions need to chose an objective measure for the particular scenario in question. As opposed to other problems in network epidemiology, we draw similar conclusions from the static and temporal networks. Furthermore, we do not find one type of network structure that predicts the objective measures -- that depends both on the data set and the SIR parameter values.

q-bio.PE↗

arXiv2018

Navigating temporal networks

Navigation on graphs is the problem how an agent walking on the graph can get from a source to a target with limited information about the graph. The information and the way to exploit it can vary. In this paper, we study navigation on temporal networks -- networks where we have explicit information about the time of the interaction, not only who interacts with whom. We contrast a type of greedy navigation -- where agents follow paths that would have worked well in the past -- with two strategies that do not exploit the additional information. We test these on empirical temporal network data sets. The greedy navigation is indeed more efficient than the reference strategies, meaning that there are correlations in the real temporal networks that can be exploited. We find that the navigability for individual nodes is most strongly correlated with degree and burstiness, i.e., both topological and temporal structures affect the navigation efficiency.

q-bio.PE↗