Controlling edge dynamics in complex networks

The interaction of distinct units in physical, social, biological and technological systems naturally gives rise to complex network structures. Networks have constantly been in the focus of research for the last decade, with considerable advances in the description of their structural and dynamical properties. However, much less effort has been devoted to studying the controllability of the dynamics taking place on them. Here we introduce and evaluate a dynamical process defined on the edges of a network, and demonstrate that the controllability properties of this process significantly differ from simple nodal dynamics. Evaluation of real-world networks indicates that most of them are more controllable than their randomized counterparts. We also find that transcriptional regulatory networks are particularly easy to control. Analytic calculations show that networks with scale-free degree distributions have better controllability properties than uncorrelated networks, and positively correlated in- and out-degrees enhance the controllability of the proposed dynamics.Comment: Preprint. 24 pages, 4 figures, 2 tables. Source code available at http://github.com/ntamas/netctr

Hierarchical self-organization of non-cooperating individuals

Hierarchy is one of the most conspicuous features of numerous natural, technological and social systems. The underlying structures are typically complex and their most relevant organizational principle is the ordering of the ties among the units they are made of according to a network displaying hierarchical features. In spite of the abundant presence of hierarchy no quantitative theoretical interpretation of the origins of a multi-level, knowledge-based social network exists. Here we introduce an approach which is capable of reproducing the emergence of a multi-levelled network structure based on the plausible assumption that the individuals (representing the nodes of the network) can make the right estimate about the state of their changing environment to a varying degree. Our model accounts for a fundamental feature of knowledge-based organizations: the less capable individuals tend to follow those who are better at solving the problems they all face. We find that relatively simple rules lead to hierarchical self-organization and the specific structures we obtain possess the two, perhaps most important features of complex systems: a simultaneous presence of adaptability and stability. In addition, the performance (success score) of the emerging networks is significantly higher than the average expected score of the individuals without letting them copy the decisions of the others. The results of our calculations are in agreement with a related experiment and can be useful from the point of designing the optimal conditions for constructing a given complex social structure as well as understanding the hierarchical organization of such biological structures of major importance as the regulatory pathways or the dynamics of neural networks.Comment: Supplementary videos are to be found at http://hal.elte.hu/~nepusz/research/supplementary/hierarchy

Capturing doping attitudes by self-report declarations and implicit assessment: a methodology study

BACKGROUND: Understanding athletes' attitudes and behavioural intentions towards performance enhancement is critical to informing anti-doping intervention strategies. Capturing the complexity of these attitudes beyond verbal declarations requires indirect methods. This pilot study was aimed at developing and validating a method to assess implicit doping attitudes using an Implicit Associations Test (IAT) approach. METHODS: The conventional IAT evaluation task (categorising 'good' and 'bad' words) was combined with a novel 'doping' versus 'nutrition supplements' category pair to create a performance-enhancement related IAT protocol (PE-IAT). The difference between average response times to 'good-doping' and 'bad-doping' combinations represents an estimate of implicit attitude towards doping in relation to nutritional supplements. 111 sports and exercise science undergraduates completed the PE-IAT, the Performance Enhancement Attitude Scale (PEAS) and answered questions regarding their beliefs about doping. RESULTS: Longer response times were observed in the mixed category discrimination trials where categories 'good' and 'doping' shared the same response key (compared to 'bad-doping' combination on the same key) indicating a less favourable evaluation of doping substances. The PE-IAT measure did not correlate significantly with the declared doping attitudes (r = .181, p = .142), indicating a predictable partial dissociation. Action-oriented self-report expressed stronger associations with PE-IAT: participants who declared they would consider using doping showed significantly less implicit negativity towards banned substances (U = 109.00, p = .047). Similarly, those who reported more lenient explicit attitudes towards doping or expressly supported legalizing it, showed less implicit negativity towards doping in the sample, although neither observed differences reached statistical significance (t = 1.300, p = .198, and U = 231.00, p = .319, respectively). Known-group validation strategy yielded mixed results: while competitive sport participants scored significantly lower than non-competitive ones on the PEAS (t = -2.71, p = .008), the two groups did not differ on PE-IAT (t = -.093, p = .926). CONCLUSION: The results suggest a potential of the PE-IAT method to capture undeclared attitudes to doping and predict behaviour, which can support targeted anti-doping intervention and related research. The initial evidence of validity is promising but also indicates a need for improvement to the protocol and stimulus material

The Multi-Player Performance-Enhancing Drug Game

This paper extends classical work on economics of doping into a multi-player game setting. Apart from being among the first papers formally formulating and analysing a multi-player doping situation, we find interesting results related to different types of Nash-equilibria (NE). Based mainly on analytic results, we claim at least two different NE structures linked to the choice of prize functions. Linear prize functions provide NEs characterised by either everyone or nobody taking drugs, while non-linear prize functions lead to qualitatively different NEs with significantly more complex predictive characteristics

The role of the Self in assessing doping cognition: Implicit and explicit measures of athletes' doping-related prototype perceptions

Objectives: To examine athletes’ implicit and explicit prototype perceptions of performance enhancing substance (PES) users and non-users. Design: A cross-sectional mixed-method study. Methods: Competitive athletes from 39 sports (N=226; mean age= 27.66±9.74 years; 59% male) completed four self-report questions and two Brief Implicit Association Tests online, assessing prototype favourability and similarity of PES users and non-users. Results: Athletes explicitly associated themselves with a non-user (M= 3.13±0.92) more than a PES user (M= 0.56±0.88) and perceived a non-user (M= 89.92±14.98) more favourably than a PES user (M= 13.18±21.38). Indexing behaviour on self-reports, doping contemplators did not differ from ‘clean’ athletes in their perceptions of PES user prototypes while dopers perceived PES users favourably and similar to themselves. In comparison, doping contemplators paired the concept of 'dopers' easier with themselves than with others, while clean athletes and dopers had no preference for either pairing (D = -0.33, -0.08 and 0.01, respectively). All groups demonstrated some degree of preference for ‘good and doper’, moving from slight to moderate to strong preference in the groups of clean athletes, dopers and contemplators, respectively (D = -0.20, -0.37 and -0.80, respectively). Conclusions: Results suggest that doping contemplators may have a positive bias towards doping which is not endorsed in self-reports. Implicit preferences, along with the disparity between the implicit and explicit measures of athletes’ doping-related prototype perceptions advance understanding of doping behaviour and make a unique contribution to research methodology. Factors influencing the interplay between explicit and implicit endorsements of PES user prototypes warrant further research

Flocking algorithm for autonomous flying robots

Animal swarms displaying a variety of typical flocking patterns would not exist without underlying safe, optimal and stable dynamics of the individuals. The emergence of these universal patterns can be efficiently reconstructed with agent-based models. If we want to reproduce these patterns with artificial systems, such as autonomous aerial robots, agent-based models can also be used in the control algorithm of the robots. However, finding the proper algorithms and thus understanding the essential characteristics of the emergent collective behaviour of robots requires the thorough and realistic modeling of the robot and the environment as well. In this paper, first, we present an abstract mathematical model of an autonomous flying robot. The model takes into account several realistic features, such as time delay and locality of the communication, inaccuracy of the on-board sensors and inertial effects. We present two decentralized control algorithms. One is based on a simple self-propelled flocking model of animal collective motion, the other is a collective target tracking algorithm. Both algorithms contain a viscous friction-like term, which aligns the velocities of neighbouring agents parallel to each other. We show that this term can be essential for reducing the inherent instabilities of such a noisy and delayed realistic system. We discuss simulation results about the stability of the control algorithms, and perform real experiments to show the applicability of the algorithms on a group of autonomous quadcopters

IGraph/M: graph theory and network analysis for Mathematica

IGraph/M is an efficient general purpose graph theory and network analysis package for Mathematica. IGraph/M serves as the Wolfram Language interfaces to the igraph C library, and also provides several unique pieces of functionality not yet present in igraph, but made possible by combining its capabilities with Mathematica's. The package is designed to support both graph theoretical research as well as the analysis of large-scale empirical networks.Comment: submitted to Journal of Open Source Software on August 30, 202

Interactive network analytical tool for instantaneous bespoke interrogation of food safety notifications

Background The globalization of food supply necessitates continued advances in regulatory control measures to ensure that citizens enjoy safe and adequate nutrition. The aim of this study was to extend previous reports on network analysis relating to food notifications by including an optional filter by type of notification and in cases of contamination, by type of contaminant in the notified foodstuff. Methodology/Principal Findings A filter function has been applied to enable processing of selected notifications by contaminant or type of notification to i) capture complexity, ii) analyze trends, and iii) identify patterns of reporting activities between countries. The program rapidly assesses nations' roles as transgressor and/or detector for each category of contaminant and for the key class of border rejection. In the open access demonstration version, the majority of notifications in the Rapid Alert System for Food and Feed were categorized by contaminant type as mycotoxin (50.4%), heavy metals (10.9%) or bacteria (20.3%). Examples are given demonstrating how network analytical approaches complement, and in some cases supersede, descriptive statistics such as frequency counts, which may give limited or potentially misleading information. One key feature is that network analysis takes the relationship between transgressor and detector countries, along with number of reports and impact simultaneously into consideration. Furhermore, the indices that compliment the network maps and reflect each country's transgressor and detector activities allow comparisons to be made between (transgressing vs. detecting) as well as within (e.g. transgressing) activities. Conclusions/significance This further development of the network analysis approach to food safety contributes to a better understanding of the complexity of the effort ensuring food is safe for consumption in the European Union. The unique patterns of the interplay between detectors and transgressors, instantly revealed by our approach, could supplement the intelligence gathered by regulatory authorities and inform risk based sampling protocols

Worldwide food recall patterns over an eleven month period: A country perspective.

<p>Abstract</p> <p>Background</p> <p>Following the World Health Organization Forum in November 2007, the Beijing Declaration recognized the importance of food safety along with the rights of all individuals to a safe and adequate diet. The aim of this study is to retrospectively analyze the patterns in food alert and recall by countries to identify the principal hazard generators and gatekeepers of food safety in the eleven months leading up to the Declaration.</p> <p>Methods</p> <p>The food recall data set was collected by the Laboratory of the Government Chemist (LGC, UK) over the period from January to November 2007. Statistics were computed with the focus reporting patterns by the 117 countries. The complexity of the recorded interrelations was depicted as a network constructed from structural properties contained in the data. The analysed network properties included degrees, weighted degrees, modularity and <it>k</it>-core decomposition. Network analyses of the reports, based on 'country making report' (<it>detector</it>) and 'country reported on' (<it>transgressor</it>), revealed that the network is organized around a dominant core.</p> <p>Results</p> <p>Ten countries were reported for sixty per cent of all faulty products marketed, with the top 5 countries having received between 100 to 281 reports. Further analysis of the dominant core revealed that out of the top five transgressors three made no reports (in the order China > Turkey > Iran). The top ten detectors account for three quarters of reports with three > 300 (Italy: 406, Germany: 340, United Kingdom: 322).</p> <p>Conclusion</p> <p>Of the 117 countries studied, the vast majority of food reports are made by 10 countries, with EU countries predominating. The majority of the faulty foodstuffs originate in ten countries with four major producers making no reports. This pattern is very distant from that proposed by the Beijing Declaration which urges all countries to take responsibility for the provision of safe and adequate diets for their nationals.</p