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Multilayer Networks in a Nutshell

Complexity Digest - Sun, 07/05/2020 - 10:04

Alberto Aleta and Yamir Moreno

Annual Review of Condensed Matter Physics
Vol. 10:45-62


Complex systems are characterized by many interacting units that give rise to emergent behavior. A particularly advantageous way to study these systems is through the analysis of the networks that encode the interactions among the system constituents. During the past two decades, network science has provided many insights in natural, social, biological, and technological systems. However, real systems are often interconnected, with many interdependencies that are not properly captured by single-layer networks. To account for this source of complexity, a more general framework, in which different networks evolve or interact with each other, is needed. These are known as multilayer networks. Here, we provide an overview of the basic methodology used to describe multilayer systems as well as of some representative dynamical processes that take place on top of them. We round off the review with a summary of several applications in diverse fields of science.

Source: www.annualreviews.org

On Crashing the Barrier of Meaning in Artificial Intelligence

Complexity Digest - Sat, 07/04/2020 - 09:41

Melanie Mitchell

AI Magazine


In 1986, the mathematician and philosopher Gian-Carlo Rota wrote, “I wonder whether or when artificial intelligence will ever crash the barrier of meaning” (Rota 1986). Here, the phrase “barrier of meaning” refers to a belief about humans versus machines: Humans are able to actually understand the situations they encounter, whereas even the most advanced of today’s artificial intelligence systems do not yet have a humanlike understanding of the concepts that we are trying to teach them. This lack of understanding may underlie current limitations on the generality and reliability of modern artificial intelligence systems. In October 2018, the Santa Fe Institute held a three-day workshop, organized by Barbara Grosz, Dawn Song, and myself, called Artificial Intelligence and the Barrier of Meaning. Thirty participants from a diverse set of disciplines — artificial intelligence, robotics, cognitive and developmental psychology, animal behavior, information theory, and philosophy, among others — met to discuss questions related to the notion of understanding in living systems and the prospect for such understanding in machines. In the hope that the results of the workshop will be useful to the broader community, this article summarizes the main themes of discussion and highlights some of the ideas developed at the workshop.

Source: www.aaai.org

Reflecting on experiences of social distancing

Complexity Digest - Sat, 07/04/2020 - 09:21

Havi Carel, Matthew Ratcliffe, Tom Froese

The Lancet


One of our children, age 7 years, was asked if he wanted to talk to his friends online. “No!” he replied angrily, “what’s the point if I can’t touch them!?” While his exasperation may not be shared by all of us, it concerns something basic to human life: embodied interaction with other people. Many aspects of our lives that were once taken for granted have been profoundly altered by lockdowns and social distancing measures that are part of the response to the COVID-19 pandemic. Things as simple as hugging a friend, talking face-to-face, socialising freely, and travelling have been restricted in many countries. Even as social distancing measures are slowly relaxed, hesitation and anxiety remain. The situation has had a profound effect on our social relations. How might we better understand how people have experienced this seismic shift?

Source: www.thelancet.com

Emergence of cooperative bistability and robustness of gene regulatory networks

Complexity Digest - Fri, 07/03/2020 - 11:16

Nagata S, Kikuchi M (2020) Emergence of cooperative bistability and robustness of gene regulatory networks. PLoS Comput Biol 16(6): e1007969. https://doi.org/10.1371/journal.pcbi.1007969


Living systems have developed through a long history of Darwinian evolution. They acquired characteristic properties distinct from other physical systems; one is biological function. Another important property, which is overlooked by non-experts, is robustness to noise and mutation. Here, robustness means that a system does not lose its functionality when exposed to disturbances. Then, how do they relate to each other? In this paper, we explored this question using a toy model of gene regulatory networks (GRNs). While evolutionary simulations are usually used for such purposes, we instead generated GRNs randomly and classified them according to functionality. By requiring sensitive responses to environmental change as a function, we found that bistability emerges as a common property of highly-functional GRNs. Since this property does not depend on a particular evolutionary pathway, if the evolution was rewound and repeated over and over again, phenotypes with the same property would always evolve. At the same time, such bistable GRNs were robust to noise. We also found that GRNs robust to mutation were not extremely rare among the highly-functional GRNs. This implies that mutational robustness would be readily acquired through evolution.

Source: journals.plos.org

New and atypical combinations: An assessment of novelty and interdisciplinarity

Complexity Digest - Fri, 07/03/2020 - 09:14

Magda Fontana, Martina Ioric, Fabio Montobbio, Roberta Sinatra

Research Policy
Volume 49, Issue 7, September 2020, 104063


Novelty indicators are increasingly important for science policy. This paper challenges the indicators of novelty as an atypical combination of knowledge (Uzzi et al., 2013) and as the first appearance of a knowledge combination (Wang et al., 2017). We exploit a sample of 230,854 articles (1985 – 2005), published on 8 journals of the American Physical Society (APS) and 2.4 million citations to test the indicators using (i) a Configuration Null Model, (ii) an external validation set of articles related to Nobel Prize winning researches and APS Milestones, (iii) a set of established interdisciplinarity indicators, and (iv) the relationship with the articles’ impact. We find that novelty as the first appearance of a knowledge combination captures the key structural properties of the citation network and finds it difficult to tell novel and non-novel articles apart, while novelty as an atypical combination of knowledge overlaps with interdisciplinarity. We suggest that the policy evidence derived from these measures should be reassessed.


Source: www.sciencedirect.com

Distributed consent and its impact on privacy and observability in social networks

Complexity Digest - Thu, 07/02/2020 - 19:00

Juniper Lovato, Antoine Allard, Randall Harp, Laurent Hébert-Dufresne


Personal data is not discrete in socially-networked digital environments. A single user who consents to allow access to their own profile can thereby expose the personal data of their network connections to non-consented access. The traditional (informed individual) consent model is therefore not appropriate in online social networks where informed consent may not be possible for all users affected by data processing and where information is shared and distributed across many nodes. Here, we introduce a model of "distributed consent" where individuals and groups can coordinate by giving consent conditional on that of their network connections. We model the impact of distributed consent on the observability of social networks and find that relatively low adoption of even the simplest formulation of distributed consent would allow macroscopic subsets of online networks to preserve their connectivity and privacy. Distributed consent is of course not a silver bullet, since it does not follow data as it flows in and out of the system, but it is one of the most straightforward non-traditional models to implement and it better accommodates the fuzzy, distributed nature of online data.

Source: arxiv.org

Enhanced ability of information gathering may intensify disagreement among groups

Complexity Digest - Thu, 07/02/2020 - 16:56

Hiroki Sayama
Phys. Rev. E 102, 012303


Today’s society faces widening disagreement and conflicts among constituents with incompatible views. Escalated views and opinions are seen not only in radical ideology or extremism but also in many other scenes of our everyday life. Here we show that widening disagreement among groups may be linked to the advancement of information communication technology by analyzing a mathematical model of population dynamics in a continuous opinion space. We adopted the interaction kernel approach to model enhancement of people’s information-gathering ability and introduced a generalized nonlocal gradient as individuals’ perception kernel. We found that the characteristic distance between population peaks becomes greater as the wider range of opinions becomes available to individuals or the more attention is attracted to opinions distant from theirs. These findings may provide a possible explanation for why disagreement is growing in today’s increasingly interconnected society, without attributing its cause only to specific individuals or events.

Source: journals.aps.org

The Sci-hub Effect: Sci-hub downloads lead to more article citations

Complexity Digest - Thu, 07/02/2020 - 15:03

J.C. Correa, H. Laverde-Rojas, F. Marmolejo-Ramos, J. Tejada, Š. Bahník


Citations are often used as a metric of the impact of scientific publications. Here, we examine how the number of downloads from Sci-hub as well as various characteristics of publications and their authors predicts future citations. Using data from 12 leading journals in economics, consumer research, neuroscience, and multidisciplinary research, we found that articles downloaded from Sci-hub were cited 1.72 times more than papers not downloaded from Sci-hub and that the number of downloads from Sci-hub was a robust predictor of future citations. Among other characteristics of publications, the number of figures in a manuscript consistently predicts its future citations. The results suggest that limited access to publications may limit some scientific research from achieving its full impact.

Source: arxiv.org

The Tricky Math of COVID-19 Herd Immunity

Complexity Digest - Thu, 07/02/2020 - 14:56

Herd immunity differs from place to place, and many factors influence how it’s calculated.

Source: www.quantamagazine.org

Algorithmic Complexity of Multiplex Networks

Complexity Digest - Tue, 06/30/2020 - 14:41

Andrea Santoro and Vincenzo Nicosia
Phys. Rev. X 10, 021069 (2020)

A new measure of complexity of multilayer networks shows that these systems can encode an optimal amount of additional information compared to their single-layer counterparts and provides a powerful tool for their analysis.

Source: journals.aps.org

Random walks on networks with stochastic resetting

Complexity Digest - Mon, 06/29/2020 - 13:57

Alejandro P. Riascos, Denis Boyer, Paul Herringer, and José L. Mateos
Phys. Rev. E 101, 062147


We study random walks with stochastic resetting to the initial position on arbitrary networks. We obtain the stationary probability distribution as well as the mean and global first passage times, which allow us to characterize the effect of resetting on the capacity of a random walker to reach a particular target or to explore a finite network. We apply the results to rings, Cayley trees, and random and complex networks. Our formalism holds for undirected networks and can be implemented from the spectral properties of the random walk without resetting, providing a tool to analyze the search efficiency in different structures with the small-world property or communities. In this way, we extend the study of resetting processes to the domain of networks.

Source: journals.aps.org

Finding Patient Zero: Learning Contagion Source with Graph Neural Networks

Complexity Digest - Mon, 06/29/2020 - 13:33

Chintan Shah, Nima Dehmamy, Nicola Perra, Matteo Chinazzi, Albert-László Barabási, Alessandro Vespignani, Rose Yu


Locating the source of an epidemic, or patient zero (P0), can provide critical insights into the infection’s transmission course and allow efficient resource allocation. Existing methods use graph-theoretic centrality measures and expensive message-passing algorithms, requiring knowledge of the underlying dynamics and its parameters. In this paper, we revisit this problem using graph neural networks (GNNs) to learn P0. We establish a theoretical limit for the identification of P0 in a class of epidemic models. We evaluate our method against different epidemic models on both synthetic and a real-world contact network considering a disease with history and characteristics of COVID-19.

We observe that GNNs can identify P0 close to the theoretical bound on accuracy, without explicit input of dynamics or its parameters. In addition, GNN is over 100 times faster than classic methods for inference on arbitrary graph topologies. Our theoretical bound also shows that the epidemic is like a ticking clock, emphasizing the importance of early contact-tracing. We find a maximum time after which accurate recovery of the source becomes impossible, regardless of the algorithm used.

Source: arxiv.org

The impact of COVID-19 and strategies for mitigation and suppression in low- and middle-income countries

Complexity Digest - Sun, 06/28/2020 - 23:18

Patrick G. T. Walker, et al.

Science 12 Jun 2020:
DOI: 10.1126/science.abc0035


The ongoing COVID-19 pandemic poses a severe threat to public health worldwide. We combine data on demography, contact patterns, disease severity, and health care capacity and quality to understand its impact and inform strategies for its control. Younger populations in lower income countries may reduce overall risk but limited health system capacity coupled with closer inter-generational contact largely negates this benefit. Mitigation strategies that slow but do not interrupt transmission will still lead to COVID-19 epidemics rapidly overwhelming health systems, with substantial excess deaths in lower income countries due to the poorer health care available. Of countries that have undertaken suppression to date, lower income countries have acted earlier. However, this will need to be maintained or triggered more frequently in these settings to keep below available health capacity, with associated detrimental consequences for the wider health, well-being and economies of these countries.

Source: science.sciencemag.org

Reducing transmission of SARS-CoV-2

Complexity Digest - Sun, 06/28/2020 - 22:22

Kimberly A. Prather, Chia C. Wang, Robert T. Schooley

Science 26 Jun 2020:
Vol. 368, Issue 6498, pp. 1422-1424
DOI: 10.1126/science.abc6197


Respiratory infections occur through the transmission of virus-containing droplets (>5 to 10 µm) and aerosols (≤5 µm) exhaled from infected individuals during breathing, speaking, coughing, and sneezing. Traditional respiratory disease control measures are designed to reduce transmission by droplets produced in the sneezes and coughs of infected individuals. However, a large proportion of the spread of coronavirus disease 2019 (COVID-19) appears to be occurring through airborne transmission of aerosols produced by asymptomatic individuals during breathing and speaking (1—3). Aerosols can accumulate, remain infectious in indoor air for hours, and be easily inhaled deep into the lungs. For society to resume, measures designed to reduce aerosol transmission must be implemented, including universal masking and regular, widespread testing to identify and isolate infected asymptomatic individuals.

Source: science.sciencemag.org

Epidemics Dynamics & Control on Networks. Call for papers

Complexity Digest - Fri, 06/26/2020 - 14:41

Networks are ubiquitous in natural, technological and social systems. They offer a fertile framework for understanding and controlling the diffusion of ideas, rumors, and infectious diseases of plants, animals, and humans. Despite recent advances, many challenging scientific questions remain about the correct tools and their practical role in epidemics dynamics and effective strategies supporting public health decision making. The goal of this special issue is to offer a platform to the interdisciplinary community of scientists working on the diffusion process on networks and its plethora of applications. We hope for a broad range of topics to be covered, across theory, methodology, and application to empirical data with a special emphasis on epidemic spreading.


Important dates
Expression of interest and abstract submission: July 10, 2020
Abstract feedback notification: July 13, 2020
Paper submission deadline: September 21, 2020
Target publication: November 01, 2020

Source: appliednetsci.springeropen.com

Starlings Fly in Flocks So Dense They Look Like Sculptures 

Complexity Digest - Fri, 06/26/2020 - 13:34

Photographer Xavi Bou condenses several seconds of movement into a single frame, showing the birds’ flight—and fight.

Source: www.wired.com

Surveillance testing of SARS-CoV-2

Complexity Digest - Fri, 06/26/2020 - 13:12

Daniel B Larremore, Bryan Wilder, Evan Lester, Soraya Shehata, James M Burke, James A Hay, Milind Tambe, Michael J Mina, Roy Parker


The COVID-19 pandemic has created a public health crisis. Because SARS-CoV-2 can spread from individuals with pre-symptomatic, symptomatic, and asymptomatic infections, the re-opening of societies and the control of virus spread will be facilitated by robust surveillance, for which virus testing will often be central. After infection, individuals undergo a period of incubation during which viral titers are usually too low to detect, followed by an exponential growth of virus, leading to a peak viral load and infectiousness, and ending with declining viral levels and clearance. Given the pattern of viral load kinetics, we model surveillance effectiveness considering test sensitivities, frequency, and sample-to-answer reporting time. These results demonstrate that effective surveillance, including time to first detection and outbreak control, depends largely on frequency of testing and the speed of reporting, and is only marginally improved by high test sensitivity. We therefore conclude that surveillance should prioritize accessibility, frequency, and sample-to-answer time; analytical limits of detection should be secondary.

Source: www.medrxiv.org

The Cartoon Picture of Magnets That Has Transformed Science

Complexity Digest - Fri, 06/26/2020 - 12:55

One hundred years after it was proposed, the Ising model is used to understand everything from magnets to brains.

Source: www.quantamagazine.org

OSoMe PostDoc Wanted

Complexity Digest - Tue, 06/23/2020 - 10:00

The Observatory on Social Media (OSoMe, pronounced ‘awe•some’) at Indiana University is looking for a postdoctoral fellow to work at the intersection of computing, network, data, and media sciences with a focus on (mis/dis)information diffusion and the detection and countering of online manipulation.

Source: cnets.indiana.edu


Complexity Digest - Tue, 06/23/2020 - 08:34

This movie describes at least five different ways in which Cybernetics can change the world for the better. The plot: An effort is made to try to sabotage a meeting of beautiful minds fearing the effect that knowledge of Cybernetics can have on both Christians and Muslims, and the world economic system. A 100% educational film to teach the history and uses of Cybernetics, as for instance to redesign many pathological organizations.

Source: www.youtube.com


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