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From: Complexity Digest <[log in to unmask]>
Sent: Monday, November 09, 2020 7:03 AM
To: Barry <[log in to unmask]>
Subject: Latest Complexity Digest Posts
Learn about the latest and greatest related to complex systems research. More at https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3Dbff97bc736-26e-3D55e25a0e3e&d=DwIFaQ&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=dWPpbuHoSOyxBS3DnjI2EWzgLhuQJLWqH2BxJxKimlU&s=89YASKh2gHzadsQYV62pVEpIkgr8R8VItFqvN4A_8Tw&e=
Patterns of ties in problem-solving networks and their dynamic properties
Scientific Reports volume 10, Article number: 18137 (2020)
Understanding the functions carried out by network subgraphs is important to revealing the organizing principles of diverse complex networks. Here, we study this question in the context of collaborative problem-solving, which is central to a variety of domains from engineering and medicine to economics and social planning. We analyze the frequency of all three- and four-node subgraphs in diverse real problem-solving networks. The results reveal a strong association between a dynamic property of network subgraphs—synchronizability—and the frequency and significance of these subgraphs in problem-solving networks. In particular, we show that highly-synchronizable subgraphs are overrepresented in the networks, while poorly-synchronizable subgraphs are underrepresented, suggesting that dynamical properties affect their prevalence, and thus the global structure of networks. We propose the possibility that selective pressures that favor more synchronizable subgraphs could account for their abundance in problem-solving networks. The empirical results also show that unrelated problem-solving networks display very similar local network structure, implying that network subgraphs could represent organizational routines that enable better coordination and control of problem-solving activities. The findings could also have potential implications in understanding the functionality of network subgraphs in other information-processing networks, including biological and social networks.
The Manufacture of Political Echo Chambers by Follow Train Abuse on Twitter
Christopher Torres-Lugo, Kai-Cheng Yang, Filippo Menczer
A growing body of evidence points to critical vulnerabilities of social media, such as the emergence of partisan echo chambers and the viral spread of misinformation. We show that these vulnerabilities are amplified by abusive behaviors associated with so-called ''follow trains'' on Twitter, in which long lists of like-minded accounts are mentioned for others to follow. This leads to the formation of highly dense and hierarchical echo chambers. We present the first systematic analysis of U.S. political train networks, which involve many thousands of hyper-partisan accounts. These accounts engage in various suspicious behaviors, including some that violate platform policies: we find evidence of inauthentic automated accounts, artificial inflation of friends and followers, and abnormal content deletion. The networks are also responsible for amplifying toxic content from low-credibility and conspiratorial sources. Platforms may be reluctant to curb this kind of abuse for fear of being accused of political bias. As a result, the political echo chambers manufactured by follow trains grow denser and train accounts accumulate influence; even political leaders occasionally engage with them.
Shared Partisanship Dramatically Increases Social Tie Formation in a Twitter Field Experiment
Mohsen Mosleh, Cameron Martel, Dean Eckles, David G. Rand
Americans are much more likely to be socially connected to co-partisans, both in daily life and on social media. But this observation does not necessarily mean that shared partisanship per se drives social tie formation, because partisanship is confounded with many other factors. Here, we test the causal effect of shared partisanship on the formation of social ties in a field experiment on Twitter. We created bot accounts that self-identified as people who favored the Democratic or Republican party, and that varied in the strength of that identification. We then randomly assigned 842 Twitter users to be followed by one of our accounts. Users were roughly three times more likely to reciprocally follow-back bots whose partisanship matched their own, and this was true regardless of the bot’s strength of identification. Interestingly, there was no partisan asymmetry in this preferential follow-back behavior: Democrats and Republicans alike were much more likely to reciprocate follows from co-partisans. These results demonstrate a strong causal effect of shared partisanship on the formation of social ties in an ecologically valid field setting, and have important implications for political psychology, social media, and the politically polarized state of the American public.
Source: psyarxiv.com (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D96adb0c832-26e-3D55e25a0e3e&d=DwIFaQ&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=dWPpbuHoSOyxBS3DnjI2EWzgLhuQJLWqH2BxJxKimlU&s=JqnmQWq2EeSCKG9qaGhV44uXxtuM4XEGfdgkgdaGzuM&e= )
Symmetry-Independent Stability Analysis of Synchronization Patterns
Yuanzhao Zhang and Adilson E. Motter
SIAM Rev., 62(4), 817–836.
The field of network synchronization has seen tremendous growth following the introduction of the master stability function (MSF) formalism, which enables the efficient stability analysis of synchronization in large oscillator networks. However, to make further progress we must overcome the limitations of this celebrated formalism, which focuses on global synchronization and requires both the oscillators and their interaction functions to be identical, while many systems of interest are inherently heterogeneous and exhibit complex synchronization patterns. Here, we establish a generalization of the MSF formalism that can characterize the stability of any cluster synchronization pattern, even when the oscillators and/or their interaction functions are nonidentical. The new framework is based on finding the finest simultaneous block diagonalization of matrices in the variational equation and does not rely on information about network symmetry. This leads to an algorithm that is error-tolerant and orders of magnitude faster than existing symmetry-based algorithms. As an application, we rigorously characterize the stability of chimera states in networks with multiple types of interactions.
Exploring the Dynamic Organization of Random and Evolved Boolean Networks
Gianluca d’Addese, Salvatore Magrì, Roberto Serra, and Marco Villani
Algorithms 2020, 13(11), 272
The properties of most systems composed of many interacting elements are neither determined by the topology of the interaction network alone, nor by the dynamical laws in isolation. Rather, they are the outcome of the interplay between topology and dynamics. In this paper, we consider four different types of systems with critical dynamic regime and with increasingly complex dynamical organization (loosely defined as the emergent property of the interactions between topology and dynamics) and analyze them from a structural and dynamic point of view. A first noteworthy result, previously hypothesized but never quantified so far, is that the topology per se induces a notable increase in dynamic organization. A second observation is that evolution does not change dramatically the size distribution of the present dynamic groups, so it seems that it keeps track of the already present organization induced by the topology. Finally, and similarly to what happens in other applications of evolutionary algorithms, the types of dynamic changes strongly depend upon the used fitness function.
Source: https://urldefense.proofpoint.com/v2/url?u=http-3A__www.mdpi.com&d=DwIFaQ&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=dWPpbuHoSOyxBS3DnjI2EWzgLhuQJLWqH2BxJxKimlU&s=P3McUUGSddJNHA7_-N-oyw152V03FQtIwjoNgWNDL7w&e= (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D1e444063d4-26e-3D55e25a0e3e&d=DwIFaQ&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=dWPpbuHoSOyxBS3DnjI2EWzgLhuQJLWqH2BxJxKimlU&s=BdFKsibkI05HDV4zSRtYPAY2LZ0k9kwUBnwr0zfLlQA&e= )
The distribution of inhibitory neurons in the C. elegans connectome facilitates self-optimization of coordinated neural activity
Alejandro Morales, Tom Froese
The nervous system of the nematode soil worm Caenorhabditis elegans exhibits remarkable complexity despite the worm's small size. A general challenge is to better understand the relationship between neural organization and neural activity at the system level, including the functional roles of inhibitory connections. Here we implemented an abstract simulation model of the C. elegans connectome that approximates the neurotransmitter identity of each neuron, and we explored the functional role of these physiological differences for neural activity. In particular, we created a Hopfield neural network in which all of the worm's neurons characterized by inhibitory neurotransmitters are assigned inhibitory outgoing connections. Then, we created a control condition in which the same number of inhibitory connections are arbitrarily distributed across the network. A comparison of these two conditions revealed that the biological distribution of inhibitory connections facilitates the self-optimization of coordinated neural activity compared with an arbitrary distribution of inhibitory connections.
Source: arxiv.org (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3Da5a2a1e09c-26e-3D55e25a0e3e&d=DwIFaQ&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=dWPpbuHoSOyxBS3DnjI2EWzgLhuQJLWqH2BxJxKimlU&s=a6yyV8DPBcAJ-5Y_wD6A3I2i0eScMqX_nQGi0k7CXLg&e= )
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