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Holme, P. (2015). Shadows of the susceptible-infectious-susceptible immortality transition in small networks. Physical Review E. Statistical, Nonlinear, and Soft Matter Physics, 92(1), Article ID 012804.
Open this publication in new window or tab >>Shadows of the susceptible-infectious-susceptible immortality transition in small networks
2015 (English)In: Physical Review E. Statistical, Nonlinear, and Soft Matter Physics, ISSN 1539-3755, E-ISSN 1550-2376, Vol. 92, no 1, article id 012804Article in journal (Refereed) Published
Abstract [en]

Much of the research on the behavior of the SIS model on networks has concerned the infinite size limit; in particular the phase transition between a state where outbreaks can reach a finite fraction of the population, and a state where only a finite number would be infected. For finite networks, there is also a dynamic transition—the immortality transition—when the per-contact transmission probability λ reaches 1. If λ < 1, the probability that an outbreak will survive by an observation time t tends to zero as t → ∞; if λ = 1, this probability is 1. We show that treating λ = 1 as a critical point predicts the λ dependence of the survival probability also for more moderate λ values. The exponent, however, depends on the underlying network. This fact could, by measuring how a vertex’s deletion changes the exponent, be used to evaluate the role of a vertex in the outbreak. Our work also confirms an extremely clear separation between the early die-off (from the outbreak failing to take hold in the population) and the later extinctions (corresponding to rare stochastic events of several consecutive transmission events failing to occur).

National Category
Physical Sciences
Identifiers
urn:nbn:se:umu:diva-107072 (URN)10.1103/PhysRevE.92.012804 (DOI)000357640900006 ()
Available from: 2015-10-13 Created: 2015-08-18 Last updated: 2018-06-07Bibliographically approved
Sircova, A., Karimi, F., Osin, E. N., Lee, S., Holme, P. & Strömbom, D. (2015). Simulating irrational human behavior to prevent resource depletion. PLoS ONE, 10(3), Article ID e0117612.
Open this publication in new window or tab >>Simulating irrational human behavior to prevent resource depletion
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2015 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 10, no 3, article id e0117612Article in journal (Refereed) Published
Abstract [en]

In a situation with a limited common resource, cooperation between individuals sharing the resource is essential. However, people often act upon self-interest in irrational ways that threaten the long-term survival of the whole group. A lack of sustainable or environmentally responsible behavior is often observed. In this study, we examine how the maximization of benefits principle works in a wider social interactive context of personality preferences in order to gain a more realistic insight into the evolution of cooperation. We used time perspective (TP), a concept reflecting individual differences in orientation towards past, present, or future, and relevant for making sustainable choices. We developed a personality-driven agent-based model that explores the role of personality in the outcomes of social dilemmas and includes multiple facets of diversity: (1) The agents have different behavior strategies: individual differences derived by applying cluster analysis to survey data from 22 countries (N = 10,940) and resulting in 7 cross-cultural profiles of TP; (2) The non-uniform distribution of the types of agents across countries; (3) The diverse interactions between the agents; and (4) diverse responses to those interactions in a well-mixed population. As one of the results, we introduced an index of overall cooperation for each of the 22 countries, which was validated against cultural, economic, and sustainability indicators (HDI, dimensions of national culture, and Environment Performance Index). It was associated with higher human development, higher individualism, lower power distance, and better environmental performance. The findings illustrate how individual differences in TP can be simulated to predict the ways people in different countries solve the personal vs. common gain dilemma in the global limited-resource situation. This interdisciplinary approach to social simulation can be adopted to explain the possible causes of global environmental issues and to predict their possible outcomes.

National Category
Biological Sciences Psychology
Identifiers
urn:nbn:se:umu:diva-102222 (URN)10.1371/journal.pone.0117612 (DOI)000351275000011 ()25760635 (PubMedID)
Available from: 2015-05-05 Created: 2015-04-22 Last updated: 2018-06-07Bibliographically approved
Holme, P. & Masuda, N. (2015). The Basic Reproduction Number as a Predictor for Epidemic Outbreaks in Temporal Networks. PLoS ONE, 10(3), Article ID e0120567.
Open this publication in new window or tab >>The Basic Reproduction Number as a Predictor for Epidemic Outbreaks in Temporal Networks
2015 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 10, no 3, article id e0120567Article in journal (Refereed) Published
Abstract [en]

The basic reproduction number R-0-the number of individuals directly infected by an infectious person in an otherwise susceptible population-is arguably the most widely used estimator of how severe an epidemic outbreak can be. This severity can be more directly measured as the fraction of people infected once the outbreak is over, Omega. In traditional mathematical epidemiology and common formulations of static network epidemiology, there is a deterministic relationship between R-0 and Omega. However, if one considers disease spreading on a temporal contact network-where one knows when contacts happen, not only between whom-then larger R-0 does not necessarily imply larger Omega. In this paper, we numerically investigate the relationship between R-0 and Omega for a set of empirical temporal networks of human contacts. Among 31 explanatory descriptors of temporal network structure, we identify those that make R-0 an imperfect predictor of Omega. We find that descriptors related to both temporal and topological aspects affect the relationship between R-0 and Omega, but in different ways.

National Category
Infectious Medicine
Identifiers
urn:nbn:se:umu:diva-103560 (URN)10.1371/journal.pone.0120567 (DOI)000352083900053 ()25793764 (PubMedID)
Available from: 2015-05-25 Created: 2015-05-21 Last updated: 2018-06-07Bibliographically approved
Holme, P. & Takaguchi, T. (2015). Time evolution of predictability of epidemics on networks. Physical Review E. Statistical, Nonlinear, and Soft Matter Physics, 91(4), Article ID 042811.
Open this publication in new window or tab >>Time evolution of predictability of epidemics on networks
2015 (English)In: Physical Review E. Statistical, Nonlinear, and Soft Matter Physics, ISSN 1539-3755, E-ISSN 1550-2376, Vol. 91, no 4, article id 042811Article in journal (Refereed) Published
Abstract [en]

Epidemic outbreaks of new pathogens, or known pathogens in new populations, cause a great deal of fear because they are hard to predict. For theoretical models of disease spreading, on the other hand, quantities characterizing the outbreak converge to deterministic functions of time. Our goal in this paper is to shed some light on this apparent discrepancy. We measure the diversity of (and, thus, the predictability of) outbreak sizes and extinction times as functions of time given different scenarios of the amount of information available. Under the assumption of perfect information-i.e., knowing the state of each individual with respect to the disease-the predictability decreases exponentially, or faster, with time. The decay is slowest for intermediate values of the per-contact transmission probability. With a weaker assumption on the information available, assuming that we know only the fraction of currently infectious, recovered, or susceptible individuals, the predictability also decreases exponentially most of the time. There are, however, some peculiar regions in this scenario where the predictability decreases. In other words, to predict its final size with a given accuracy, we would need increasingly more information about the outbreak.

National Category
Other Computer and Information Science
Identifiers
urn:nbn:se:umu:diva-103211 (URN)10.1103/PhysRevE.91.042811 (DOI)000353546300009 ()
Available from: 2015-05-22 Created: 2015-05-18 Last updated: 2018-06-07Bibliographically approved
Holme, P. (2014). Analyzing temporal networks in social media. Proceedings of the IEEE, 102(12), 1922-1933
Open this publication in new window or tab >>Analyzing temporal networks in social media
2014 (English)In: Proceedings of the IEEE, ISSN 0018-9219, E-ISSN 1558-2256, Vol. 102, no 12, p. 1922-1933Article, review/survey (Refereed) Published
Abstract [en]

Many types of social media metadata come in forms of temporal networks, networks where we have information about not only who is in contact with whom but also when contacts happen. In this paper, we review methods to analyze temporal networks developed in the last few years applied to social media data. These methods seek to identify important spreaders and, in more generality, how the temporal and topological structure of interaction affects spreading processes.

Keywords
network analysis, social network services, temporal networks
National Category
Information Systems
Identifiers
urn:nbn:se:umu:diva-96754 (URN)10.1109/JPROC.2014.2361326 (DOI)000345524100007 ()2-s2.0-84914118377 (Scopus ID)
Funder
Swedish Research Council
Note

This paper reviews methods for analyzing recently temporal networks applied to socialmedia data.

Available from: 2014-12-02 Created: 2014-12-02 Last updated: 2018-06-07Bibliographically approved
Holme, P. & Liljeros, F. (2014). Birth and death of links control disease spreading in empirical contact networks. Scientific Reports, 4, 4999
Open this publication in new window or tab >>Birth and death of links control disease spreading in empirical contact networks
2014 (English)In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 4, p. 4999-Article in journal (Refereed) Published
Abstract [en]

We investigate what structural aspects of a collection of twelve empirical temporal networks of human contacts are important to disease spreading. We scan the entire parameter spaces of the two canonical models of infectious disease epidemiology-the Susceptible-Infectious-Susceptible (SIS) and Susceptible-Infectious-Removed (SIR) models. The results from these simulations are compared to reference data where we eliminate structures in the interevent intervals, the time to the first contact in the data, or the time from the last contact to the end of the sampling. The picture we find is that the birth and death of links, and the total number of contacts over a link, are essential to predict outbreaks. On the other hand, the exact times of contacts between the beginning and end, or the interevent interval distribution, do not matter much. In other words, a simplified picture of these empirical data sets that suffices for epidemiological purposes is that links are born, is active with some intensity, and die.

National Category
Other Computer and Information Science
Identifiers
urn:nbn:se:umu:diva-90431 (URN)10.1038/srep04999 (DOI)000336263500001 ()
Available from: 2014-07-09 Created: 2014-06-23 Last updated: 2018-06-07Bibliographically approved
Mondani, H., Holme, P. & Liljeros, F. (2014). Fat-Tailed Fluctuations in the Size of Organizations: The Role of Social Influence. PLoS ONE, 9(7), Article ID e100527.
Open this publication in new window or tab >>Fat-Tailed Fluctuations in the Size of Organizations: The Role of Social Influence
2014 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 9, no 7, article id e100527Article in journal (Refereed) Published
Abstract [en]

Organizational growth processes have consistently been shown to exhibit a fatter-than-Gaussian growth-rate distribution in a variety of settings. Long periods of relatively small changes are interrupted by sudden changes in all size scales. This kind of extreme events can have important consequences for the development of biological and socio-economic systems. Existing models do not derive this aggregated pattern from agent actions at the micro level. We develop an agent-based simulation model on a social network. We take our departure in a model by a Schwarzkopf et al. on a scale-free network. We reproduce the fat-tailed pattern out of internal dynamics alone, and also find that it is robust with respect to network topology. Thus, the social network and the local interactions are a prerequisite for generating the pattern, but not the network topology itself. We further extend the model with a parameter  that weights the relative fraction of an individual's neighbours belonging to a given organization, representing a contextual aspect of social influence. In the lower limit of this parameter, the fraction is irrelevant and choice of organization is random. In the upper limit of the parameter, the largest fraction quickly dominates, leading to a winner-takes-all situation. We recover the real pattern as an intermediate case between these two extremes.

Place, publisher, year, edition, pages
Public Library Science, 2014
National Category
Social Sciences Interdisciplinary Condensed Matter Physics
Identifiers
urn:nbn:se:umu:diva-92059 (URN)10.1371/journal.pone.0100527 (DOI)000339615200008 ()25036729 (PubMedID)
Funder
Swedish Research Council, 2012-3651
Available from: 2014-08-21 Created: 2014-08-21 Last updated: 2018-06-07Bibliographically approved
Holme, P. (2014). Model versions and fast algorithms for network epidemiology. Journal of Logistical Engineering University, 30(3), 1-7
Open this publication in new window or tab >>Model versions and fast algorithms for network epidemiology
2014 (English)In: Journal of Logistical Engineering University, ISSN 1672-7843, Vol. 30, no 3, p. 1-7Article in journal (Refereed) Published
Abstract [en]

Network epidemiology has become a core framework for investigating the role of human contact patterns in the spreading of infectious diseases. In network epidemiology, one represents the contact structure as a network of nodes (individuals) connected by links (sometimes as a temporal network where the links are not continuously active) and the disease as a compartmental model (where individuals are assigned states with respect to the disease and follow certain transition rules between the states). In this paper, we discuss fast algorithms for such simulations and also compare two commonly used versions,one where there is a constant recovery rate (the number of individuals that stop being infectious per time is proportional to the number of such people);the other where the duration of the disease is constant. The results show that, for most practical purposes, these versions are qualitatively the same.

National Category
Other Natural Sciences Computer Sciences Other Medical Sciences not elsewhere specified
Identifiers
urn:nbn:se:umu:diva-92060 (URN)10.3969/j.issn.1672-7843.2014.03.001 (DOI)
Funder
Swedish Research Council, 2012-3651
Available from: 2014-08-21 Created: 2014-08-21 Last updated: 2018-06-07Bibliographically approved
Karimi, F., Ramenzoni, V. & Holme, P. (2014). Structural differences between open and direct communication in an online community. Physica A: Statistical Mechanics and its Applications, 414(15 November), 263-273
Open this publication in new window or tab >>Structural differences between open and direct communication in an online community
2014 (English)In: Physica A: Statistical Mechanics and its Applications, ISSN 0378-4371, E-ISSN 1873-2119, Vol. 414, no 15 November, p. 263-273Article in journal (Refereed) Published
Abstract [en]

Most research of online communication focuses on modes of communication that are either open (like forums, bulletin boards, Twitter, etc.) or direct (like e-mails). In this work, we study a dataset that has both types of communication channels. We relate our findings to theories of social organization and human dynamics. The data comprises 36,492 users of a movie discussion community. Our results show that there are differences in the way users communicate in the two channels that are reflected in the shape of degree- and interevent time distributions. The open communication that is designed to facilitate conversations with any member shows a broader degree distribution and more of the triangles in the network are primarily formed in this mode of communication. The direct channel is presumably preferred by closer communication and the response time in dialogs is shorter. On a more coarse-grained level, there are common patterns in the two networks. The differences and overlaps between communication networks, thus, provide a unique window into how social and structural aspects of communication establish and evolve.

Place, publisher, year, edition, pages
Elsevier, 2014
Keywords
multiplex networks, network theory, communication motifs
National Category
Other Physics Topics Communication Systems Communication Studies
Identifiers
urn:nbn:se:umu:diva-92058 (URN)10.1016/j.physa.2014.07.037 (DOI)000342253300028 ()2-s2.0-84906254086 (Scopus ID)
Funder
Swedish Research Council, 2012-3651
Available from: 2014-08-21 Created: 2014-08-21 Last updated: 2018-06-07Bibliographically approved
Holme, P. (2014). Temporal networks. In: Reda Alhajj, Jon Rokne (Ed.), Encyclopedia of Social Network Analysis and Mining: (pp. 2119-2128). Springer-Verlag New York
Open this publication in new window or tab >>Temporal networks
2014 (English)In: Encyclopedia of Social Network Analysis and Mining / [ed] Reda Alhajj, Jon Rokne, Springer-Verlag New York, 2014, p. 2119-2128Chapter in book (Refereed)
Place, publisher, year, edition, pages
Springer-Verlag New York, 2014
National Category
Other Computer and Information Science
Identifiers
urn:nbn:se:umu:diva-93197 (URN)978-1-4614-6169-2 (ISBN)
Funder
Swedish Research Council, 2012-3651
Available from: 2014-09-12 Created: 2014-09-12 Last updated: 2018-06-07Bibliographically approved
Projects
Statistical physics of complex networks [2009-03536_VR]; Umeå UniversityStatistical physics of temporal networks [2012-03651_VR]; Umeå University
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-2156-1096

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