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NETWORKED: The New Social Operating System  Lee Rainie & Barry Wellman    

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Subject: 	Latest Complexity Digest Posts
Date: 	Mon, 6 Jul 2020 11:03:50 +0000
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Learn about the latest and greatest related to complex systems research. 
More at 

Multilayer Networks in a Nutshell

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.

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Emergence of cooperative bistability and robustness of gene regulatory 

Nagata S, Kikuchi M (2020) Emergence of cooperative bistability and 
robustness of gene regulatory networks. PLoS Comput Biol 16(6): 

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.

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New and atypical combinations: An assessment of novelty and 

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.

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

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.

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Enhanced ability of information gathering may intensify disagreement 
among groups

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.

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

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.

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The Tricky Math of COVID-19 Herd Immunity 

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

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Algorithmic Complexity of Multiplex Networks 

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.

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Random walks on networks with stochastic resetting

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.

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Finding Patient Zero: Learning Contagion Source with Graph Neural Networks

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.

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Sponsored by the Complex Systems Society.
Founding Editor: Gottfried Mayer.
Editor-in-Chief: Carlos Gershenson.

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