***** To join INSNA, visit *****

See you virtually at the Sunbelt soon


Barry Wellman, FRSC               Director, NetLab Network
Founder, International Network for Social Network Analysis

Bit by bit, putting it together--Sondheim
It's Always Something--Roseanne Roseannadanna

Getting It Done; Getting It Out: A Practical Guide to Writing, Publishing, Presenting, and Promoting in the Social Sciences--coming in 2021 (Guilford Press)

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

-------- Forwarded Message --------
Subject: Latest Complexity Digest Posts
Date: Mon, 6 Jul 2020 11:03:50 +0000
From: Complexity Digest <[log in to unmask]>
Reply-To: [log in to unmask]
To: Barry <[log in to unmask]>

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.

Source: (

Emergence of cooperative bistability and robustness of gene regulatory networks

Nagata S, Kikuchi M (2020) Emergence of cooperative bistability and robustness of gene regulatory networks. PLoS Comput Biol 16(6): e1007969.

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: (

New and atypical combinations: An assessment of novelty and interdisciplinarity

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: (

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.

Source: (

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.

Source: (

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.

Source: (

The Tricky Math of COVID-19 Herd Immunity

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

Source: (

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.

Source: (

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.

Source: (

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.

Source: (

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

You can contribute to Complexity Digest selecting one of our topics ( ) and using the "Suggest" button.

_____________________________________________________________________ SOCNET is a service of INSNA, the professional association for social network researchers ( To unsubscribe, send an email message to [log in to unmask] containing the line UNSUBSCRIBE SOCNET in the body of the message.