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Controllability and observability analysis for vertex domination centrality in directed networks

Topological centrality is a significant measure for characterising the relative importance of a node in a complex network. For directed networks that model dynamic processes, however, it is of more practical importance to quantify a vertex's ability to dominate (control or observe) the state of other vertices. In this paper, based on the determination of controllable and observable subspaces under the global minimum-cost condition, we introduce a novel direction-specific index, domination centrality, to assess the intervention capabilities of vertices in a directed network. Statistical studies demonstrate that the domination centrality is, to a great extent, encoded by the underlying network's degree distribution and that most network positions through which one can intervene in a system are vertices with high domination centrality rather than network hubs. To analyse the interaction and functional dependence between vertices when they are used to dominate a network, we define the domination similarity and detect significant functional modules in glossary and metabolic networks through clustering analysis. The experimental results provide strong evidence that our indices are effective and practical in accurately depicting the structure of directed networks.

 

Controllability and observability analysis for vertex domination centrality in directed networks
B Wang, L Gao, Y Gao, Y Deng, and Y Wang

Scientific Reports 4, Article number 5399 (23 June 2014)

http://dx.doi.org/10.1038/srep05399

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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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Reflecting on experiences of social distancing

Reflecting on experiences of social distancing | Papers | Scoop.it

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?

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New and atypical combinations: An assessment of novelty and interdisciplinarity

New and atypical combinations: An assessment of novelty and interdisciplinarity | Papers | Scoop.it

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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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 Tricky Math of COVID-19 Herd Immunity

The Tricky Math of COVID-19 Herd Immunity | Papers | Scoop.it
Herd immunity differs from place to place, and many factors influence how it’s calculated.
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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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The impact of COVID-19 and strategies for mitigation and suppression in low- and middle-income countries

Patrick G. T. Walker, et al.

Science 12 Jun 2020:
eabc0035
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.

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Starlings Fly in Flocks So Dense They Look Like Sculptures 

Starlings Fly in Flocks So Dense They Look Like Sculptures  | Papers | Scoop.it
Photographer Xavi Bou condenses several seconds of movement into a single frame, showing the birds' flight—and fight.
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The Cartoon Picture of Magnets That Has Transformed Science

The Cartoon Picture of Magnets That Has Transformed Science | Papers | Scoop.it
One hundred years after it was proposed, the Ising model is used to understand everything from magnets to brains.
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Information arms race explains plant-herbivore chemical communication in ecological communities

Pengjuan Zu, Karina Boege, Ek del-Val, Meredith C. Schuman, Philip C. Stevenson, Alejandro Zaldivar-Riverón, Serguei Saavedra

Science  19 Jun 2020:
Vol. 368, Issue 6497, pp. 1377-1381
DOI: 10.1126/science.aba2965

 

Plants emit an extraordinary diversity of chemicals that provide information about their identity and mediate their interactions with insects. However, most studies of this have focused on a few model species in controlled environments, limiting our capacity to understand plant-insect chemical communication in ecological communities. Here, by integrating information theory with ecological and evolutionary theories, we show that a stable information structure of plant volatile organic compounds (VOCs) can emerge from a conflicting information process between plants and herbivores. We corroborate this information “arms race” theory with field data recording plant-VOC associations and plant-herbivore interactions in a tropical dry forest. We reveal that plant VOC redundancy and herbivore specialization can be explained by a conflicting information transfer. Information-based communication approaches can increase our understanding of species interactions across trophic levels.

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Flow-Mediated Olfactory Communication in Honey Bee Swarms

Dieu My T. Nguyen, Michael L. Iuzzolino, Aaron Mankel, Katarzyna Bozek, Greg J. Stephens, Orit Peleg

 

Honey bee swarms are a landmark example of collective behavior. To become a coherent swarm, bees locate their queen by tracking her pheromones, but how can distant individuals exploit these chemical signals which decay rapidly in space and time? Here, we combine a novel behavioral assay with the machine vision detection of organism location and scenting behavior to track the search and aggregation dynamics of the honey bee Apis mellifera L. We find that bees collectively create a communication network to propagate pheromone signals, by arranging in a specific spatial distribution where there is a characteristic distance between individuals and a characteristic direction in which individuals broadcast the signals. To better understand such a flow–mediated directional communication strategy, we connect our experimental results to an agent–based model where virtual bees with simple, local behavioral rules, exist in a flow environment. Our model shows that increased directional bias leads to a more efficient aggregation process that avoids local equilibrium configurations of isotropic communication, such as small bee clusters that persist throughout the simulation. Our results highlight a novel example of extended classical stigmergy: rather than depositing static information in the environment, individual bees locally sense and globally manipulate the physical fields of chemical concentration and airflow.

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Data-Driven Learning of Boolean Networks and Functions by Optimal Causation Entropy Principle (BoCSE)

Jie Sun, Abd AlRahman AlMomani, Erik Bollt

 

Boolean functions and networks are commonly used in the modeling and analysis of complex biological systems, and this paradigm is highly relevant in other important areas in data science and decision making, such as in the medical field and in the finance industry. Automated learning of a Boolean network and Boolean functions, from data, is a challenging task due in part to the large number of unknowns (including both the structure of the network and the functions) to be estimated, for which a brute force approach would be exponentially complex. In this paper we develop a new information theoretic methodology that we show to be significantly more efficient than previous approaches. Building on the recently developed optimal causation entropy principle (oCSE), that we proved can correctly infer networks distinguishing between direct versus indirect connections, we develop here an efficient algorithm that furthermore infers a Boolean network (including both its structure and function) based on data observed from the evolving states at nodes. We call this new inference method, Boolean optimal causation entropy (BoCSE), which we will show that our method is both computationally efficient and also resilient to noise. Furthermore, it allows for selection of a set of features that best explains the process, a statement that can be described as a networked Boolean function reduced order model. We highlight our method to the feature selection in several real-world examples: (1) diagnosis of urinary diseases, (2) Cardiac SPECT diagnosis, (3) informative positions in the game Tic-Tac-Toe, and (4) risk causality analysis of loans in default status. Our proposed method is effective and efficient in all examples.

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Living models or life modelled? On the use of models in the free energy principle

Thomas van Es
Adaptive Behavior

 

The free energy principle (FEP) is an information-theoretic approach to living systems. FEP characterizes life by living systems’ resistance to the second law of thermodynamics: living systems do not randomly visit the possible states, but actively work to remain within a set of viable states. In FEP, this is modelled mathematically. Yet, the status of these models is typically unclear: are these models employed by organisms or strictly scientific tools of understanding? In this article, I argue for an instrumentalist take on models in FEP. I shall argue that models used as instruments for knowledge by scientists and models as implemented by organisms to navigate the world are being conflated, which leads to erroneous conclusions. I further argue that a realist position is unwarranted. First, it overgenerates models and thus trivializes the notion of modelling. Second, even when the mathematical mechanisms described by FEP are implemented in an organism, they do not constitute a model. They are covariational, not representational in nature, and precede the social practices that have shaped our scientific modelling practice. I finally argue that the above arguments do not affect the instrumentalist position. An instrumentalist approach can further add to conceptual clarity in the FEP literature.

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On Crashing the Barrier of Meaning in Artificial Intelligence

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.

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Emergence of cooperative bistability and robustness of gene regulatory networks

Emergence of cooperative bistability and robustness of gene regulatory networks | Papers | Scoop.it

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.

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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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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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Algorithmic Complexity of Multiplex Networks

Algorithmic Complexity of Multiplex Networks | Papers | Scoop.it

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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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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Reducing transmission of SARS-CoV-2

Reducing transmission of SARS-CoV-2 | Papers | Scoop.it

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.

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Surveillance testing of SARS-CoV-2

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.

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Precise mapping, spatial structure and classification of all the human settlements on Earth

Precise mapping, spatial structure and classification of all the human settlements on Earth
Emanuele Strano, Filippo Simini, Marco De Nadai, Thomas Esch, Mattia Marconcini

 

Human settlements (HSs) on Earth have a direct impact on all natural and societal systems but detailed and quantitative measurements of the locations and spatial structures of all HSs on Earth are still under debate. We provide here the World Settlement Footprint 2015, an unprecedented 10 m resolution global spatial inventory of HSs and a precise quantitative analysis and spatial model of their coverage, geography and morphology. HSs are estimated to cover 1.47% of the habitable global dry-land surface and can be classified, by means of their deviation from scaling structure, into four main pattern typologies. A minimal spatial model, based on dynamic interactions between dispersal and centralized urbanization, is able to reproduce all the settlement patterns across regions. Our dataset and settlement model can be used to improve the modelling of global land use changes and human land use cycles and interactions and can ultimately advance our understanding of global anthropization processes and human-induced environmental changes.

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Globalization and the rise and fall of cognitive control

Globalization and the rise and fall of cognitive control | Papers | Scoop.it

Mohsen Mosleh, Katelynn Kyker, Jonathan D. Cohen & David G. Rand

Nature Communications volume 11, Article number: 3099 (2020)

 

The scale of human interaction is larger than ever before—people regularly interact with and learn from others around the world, and everyone impacts the global environment. We develop an evolutionary game theory model to ask how the scale of interaction affects the evolution of cognition. Our agents make decisions using automatic (e.g., reflexive) versus controlled (e.g., deliberative) cognition, interact with each other, and influence the environment (i.e., game payoffs). We find that globalized direct contact between agents can either favor or disfavor control, depending on whether controlled agents are harmed or helped by contact with automatic agents; globalized environment disfavors cognitive control, while also promoting strategic diversity and fostering mesoscale communities of more versus less controlled agents; and globalized learning destroys mesoscale communities and homogenizes the population. These results emphasize the importance of the scale of interaction for the evolution of cognition, and help shed light on modern challenges. Humankind is in a period of unprecedented cognitive sophistication as well as globalization. Here, using an evolutionary game theory model, the authors reveal ways in which the transition from local to global interaction can have both positive and potentially negative consequences for the prevalence of cognitive sophistication in the population.

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Uncovering the internal structure of Boko Haram through its mobility patterns

Rafael Prieto Curiel, Olivier Walther & Neave O’Clery
Applied Network Science volume 5, Article number: 28 (2020)

 

Boko Haram has caused nearly 40,000 casualties in Nigeria, Niger, Cameroon and Chad, becoming one of the deadliest Jihadist organisations in recent history. At its current rate, Boko Haram takes part in more than two events each day, taking the lives of nearly 11 people daily. Yet, little is known concerning Boko Haram’s internal structure, organisation, and its mobility.

Here, we propose a novel technique to uncover the internal structure of Boko Haram based on the sequence of events in which the terrorist group takes part. Data from the Armed Conflict Location & Event Data Project (ACLED) gives the location and time of nearly 3,800 events in which Boko Haram has been involved since the organisation became violent 10 years ago. Using this dataset, we build an algorithm to detect the fragmentation of Boko Haram into multiple cells, assuming that travel costs and reduced familiarity with unknown locations limit the mobility of individual cells.

Our results suggest that the terrorist group has a very high level of fragmentation and consists of at least 50–60 separate cells. Our methodology enables us to detect periods of time during which Boko Haram exhibits exceptionally high levels of fragmentation, and identify a number of key routes frequently travelled by separate cells of Boko Haram where military interventions could be concentrated.

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Uncovering the social interaction network in swarm intelligence algorithms

Uncovering the social interaction network in swarm intelligence algorithms | Papers | Scoop.it

Marcos Oliveira, Diego Pinheiro, Mariana Macedo, Carmelo Bastos-Filho & Ronaldo Menezes
Applied Network Science volume 5, Article number: 24 (2020)

 

Swarm intelligence is the collective behavior emerging in systems with locally interacting components. Because of their self-organization capabilities, swarm-based systems show essential properties for handling real-world problems, such as robustness, scalability, and flexibility. Yet, we fail to understand why swarm-based algorithms work well, and neither can we compare the various approaches in the literature. The absence of a common framework capable of characterizing these several swarm-based algorithms, transcending their particularities, has led to a stream of publications inspired by different aspects of nature without a systematic comparison over existing approaches. Here we address this gap by introducing a network-based framework—the swarm interaction network—to examine computational swarm-based systems via the optics of the social dynamics. We investigate the structure of social interaction in four swarm-based algorithms, showing that our approach enables researchers to study distinct algorithms from a common viewpoint. We also provide an in-depth case study of the Particle Swarm Optimization, revealing that different communication schemes tune the social interaction in the swarm, controlling the swarm search mode. With the swarm interaction network, researchers can study swarm algorithms as systems, removing the algorithm particularities from the analyses while focusing on the structure of the swarm social interaction.

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