Macro and micro network metrics as indicators of training tasks adjustment to players’ tactical level

2020 ◽  
pp. 174795412097956
Author(s):  
João Cláudio Machado ◽  
Rodrigo Aquino ◽  
Alberto Góes Júnior ◽  
João Bosco Júnior ◽  
Daniel Barreira ◽  
...  
Keyword(s):  
Social Networks ◽  
Clustering Coefficient ◽  
Soccer Players ◽  
Interaction Patterns ◽  
Training Context ◽  
Network Metrics ◽  
Level Group

We aimed to investigate if social networks measures can be used as indicators of training tasks' adjustment level to soccer players’ tactical skills. Twenty-four U17 male soccer players (16.89 ± 0.11 years) participated in this study. The System of Tactical Assessment in Football (FUT-SAT) was used to identify players’ tactical level and to organize them into three groups: Higher tactical level (Group 01), Intermediate tactical level (Group 02) and Lower tactical level (Group 03). Then, the players performed three High difficulty Small-Sided and Conditioned Games (HD-SSCG) and three Low difficulty Small-Sided and Conditioned Games (LD-SSCG). Teams’ interaction patterns and players’ prominence were analysed based on macro (Density – D and Clustering coefficient – CC) and micro networks (Indegree, Outdegree, Total links and Eigenvector) measures. We found that Group 01 presented higher D (p = .004 and ES = 1.189) and CC (p =.004 and ES = .785) at HD-SSCG than Group 03, whereas Group 03 presented higher values of D (p = .003 and ES = 1.200) and CC (p = .037 and ES = 1.180) at LD-SSCG than Group 01. When training tasks difficulty were adjusted to players’ tactical level, teams played more collectively and players were more actively engaged in ball circulation. We concluded that macro and micro networks measures can be applied in training context as indicators of training tasks adjustment to players' tactical level.

scholarly journals Emergence and evolution of social networks through exploration of the Adjacent Possible space

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Enrico Ubaldi ◽  
Raffaella Burioni ◽  
Vittorio Loreto ◽  
Francesca Tria
Keyword(s):  
Social Networks ◽  
Community Structure ◽  
Social Interactions ◽  
Clustering Coefficient ◽  
Human Beings ◽  
Broad Distribution ◽  
Phone Calls ◽  
Dynamical Features ◽  
Twitter Users ◽  
American Physical

AbstractThe interactions among human beings represent the backbone of our societies. How people establish new connections and allocate their social interactions among them can reveal a lot of our social organisation. We leverage on a recent mathematical formalisation of the Adjacent Possible space to propose a microscopic model accounting for the growth and dynamics of social networks. At the individual’s level, our model correctly reproduces the rate at which people acquire new acquaintances as well as how they allocate their interactions among existing edges. On the macroscopic side, the model reproduces the key topological and dynamical features of social networks: the broad distribution of degree and activities, the average clustering coefficient and the community structure. The theory is born out in three diverse real-world social networks: the network of mentions between Twitter users, the network of co-authorship of the American Physical Society journals, and a mobile-phone-calls network.

scholarly journals Automated monitoring of behavior reveals bursty interaction patterns and rapid spreading dynamics in honeybee social networks

2018 ◽  
Vol 115 (7) ◽  
pp. 1433-1438 ◽  
Author(s):  
Tim Gernat ◽  
Vikyath D. Rao ◽  
Martin Middendorf ◽  
Harry Dankowicz ◽  
Nigel Goldenfeld ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Interactions ◽  
Communication Networks ◽  
Disease Transmission ◽  
Large Scale ◽  
Reference Network ◽  
Interaction Patterns ◽  
Human Communication ◽  
Automated Monitoring ◽  
Phone Calls

Social networks mediate the spread of information and disease. The dynamics of spreading depends, among other factors, on the distribution of times between successive contacts in the network. Heavy-tailed (bursty) time distributions are characteristic of human communication networks, including face-to-face contacts and electronic communication via mobile phone calls, email, and internet communities. Burstiness has been cited as a possible cause for slow spreading in these networks relative to a randomized reference network. However, it is not known whether burstiness is an epiphenomenon of human-specific patterns of communication. Moreover, theory predicts that fast, bursty communication networks should also exist. Here, we present a high-throughput technology for automated monitoring of social interactions of individual honeybees and the analysis of a rich and detailed dataset consisting of more than 1.2 million interactions in five honeybee colonies. We find that bees, like humans, also interact in bursts but that spreading is significantly faster than in a randomized reference network and remains so even after an experimental demographic perturbation. Thus, while burstiness may be an intrinsic property of social interactions, it does not always inhibit spreading in real-world communication networks. We anticipate that these results will inform future models of large-scale social organization and information and disease transmission, and may impact health management of threatened honeybee populations.

scholarly journals Cognitive function, disease burden and the structural connectome in systemic lupus erythematosus

Lupus ◽  
2018 ◽  
Vol 27 (8) ◽  
pp. 1329-1337 ◽  
Author(s):  
S J Wiseman ◽  
M E Bastin ◽  
E N Amft ◽  
J F F Belch ◽  
S H Ralston ◽  
...  
Keyword(s):  
Systemic Lupus Erythematosus ◽  
Cognitive Function ◽  
Lupus Erythematosus ◽  
Cognitive Abilities ◽  
Structural Connectivity ◽  
Principal Component ◽  
Clustering Coefficient ◽  
Screening Tests ◽  
Systemic Lupus ◽  
Network Metrics

Objective To investigate brain structural connectivity in relation to cognitive abilities and systemic damage in systemic lupus erythematosus (SLE). Methods Structural and diffusion MRI data were acquired from 47 patients with SLE. Brains were segmented into 85 cortical and subcortical regions and combined with whole brain tractography to generate structural connectomes using graph theory. Global cognitive abilities were assessed using a composite variable g, derived from the first principal component of three common clinical screening tests of neurological function. SLE damage ( LD) was measured using a composite of a validated SLE damage score and disease duration. Relationships between network connectivity metrics, cognitive ability and systemic damage were investigated. Hub nodes were identified. Multiple linear regression, adjusting for covariates, was employed to model the outcomes g and LD as a function of network metrics. Results The network measures of density (standardised ß = 0.266, p = 0.025) and strength (standardised ß = 0.317, p = 0.022) were independently related to cognitive abilities. Strength (standardised ß = –0.330, p = 0.048), mean shortest path length (standardised ß = 0.401, p = 0.020), global efficiency (standardised ß = –0.355, p = 0.041) and clustering coefficient (standardised ß = –0.378, p = 0.030) were independently related to systemic damage. Network metrics were not related to current disease activity. Conclusion Better cognitive abilities and more SLE damage are related to brain topological network properties in this sample of SLE patients, even those without neuropsychiatric involvement and after correcting for important covariates. These data show that connectomics might be useful for understanding and monitoring cognitive function and white matter damage in SLE.

scholarly journals Destination Value Systems: Modeling Visitor Flow Structure and Economic Impact

2018 ◽  
Vol 58 (8) ◽  
pp. 1249-1261 ◽  
Author(s):  
Jason L. Stienmetz ◽  
Daniel R. Fesenmaier
Keyword(s):  
Positive Relationship ◽  
Economic Value ◽  
Clustering Coefficient ◽  
Value Systems ◽  
Supply Side ◽  
Regression Analyses ◽  
Florida Department ◽  
Photo Sharing ◽  
Geotagged Photos ◽  
Network Metrics

This study proposes that the structure of visitor flows within a destination significantly influences the overall economic value generated by visitors. In particular, destination network metrics (i.e., density, in-degree centralization, out-degree centralization, betweenness centralization, and global clustering coefficient) for 29 Florida counties were derived from 4.3 million geotagged photos found on the photo sharing service Flickr and then correlated with visitor-related spending reported by the Florida Department of Revenue. The results of regression analyses indicate that density, out-degree centralization, and in-degree centralization are negatively correlated with total visitor-related spending within a destination, while betweenness centralization is found to have a positive relationship. Based on these findings, it is concluded that the economic value generated by tourism is constrained by the destination network structure of supply-side and demand-side interactions. Further, it is argued that a “network orchestrator” approach to management can be used to better manage economic impacts within a destination.

The Ability of Runners to Identify Spatial and Temporal Variables of Speed During Endurance Running

Motor Control ◽  
2020 ◽  
Vol 24 (4) ◽  
pp. 499-511
Author(s):  
Pedro Ángel Latorre-Román ◽  
Juan Francisco Fernández-Povedano ◽  
Jesús Salas-Sánchez ◽  
Felipe García-Pinillos ◽  
Juan Antonio Párraga-Montilla
Keyword(s):  
Spatial Perception ◽  
Cognitive Skill ◽  
Soccer Players ◽  
Temporal Perception ◽  
Endurance Running ◽  
Temporal Variables ◽  
Temporal And Spatial ◽  
Endurance Runners ◽  
And Performance ◽  
Level Group

This study aimed to evaluate spatial and temporal perception in endurance runners as a mechanism of pacing control in comparison with other athletes (soccer players). A group of 38 endurance runners and 32 soccer players participated in this study. Runners displayed lower time differences and lower error than soccer players. Taking the athletic levels of endurance runners into consideration, significant differences (p = .011, Cohen’s d = 1.042) were found in the time differences (higher level group = 33.43 ± 29.43 vs. lower level group = 123.53 ±102.61). Significant correlations were found between time differences and performance in a Cooper test (r = −.546) and with the best time in a half marathon (r = .597). Temporal and spatial perception can be considered as a cognitive skill of endurance runners.

scholarly journals Information Integration and Mesoscopic Cortical Connectivity during Propofol Anesthesia

2020 ◽  
Vol 132 (3) ◽  
pp. 504-524 ◽  
Author(s):  
Zhenhu Liang ◽  
Lei Cheng ◽  
Shuai Shao ◽  
Xing Jin ◽  
Tao Yu ◽  
...  
Keyword(s):  
Mutual Information ◽  
Information Integration ◽  
Path Length ◽  
Cortical Network ◽  
Clustering Coefficient ◽  
Loss Of Consciousness ◽  
Cortical Connectivity ◽  
Cortical Regions ◽  
Network Metrics ◽  
Unconscious State

Abstract Background The neurophysiologic mechanisms of propofol-induced loss of consciousness have been studied in detail at the macro (scalp electroencephalogram) and micro (spiking or local field potential) scales. However, the changes in information integration and cortical connectivity during propofol anesthesia at the mesoscopic level (the cortical scale) are less clear. Methods The authors analyzed electrocorticogram data recorded from surgical patients during propofol-induced unconsciousness (n = 9). A new information measure, genuine permutation cross mutual information, was used to analyze how electrocorticogram cross-electrode coupling changed with electrode-distances in different brain areas (within the frontal, parietal, and temporal regions, as well as between the temporal and parietal regions). The changes in cortical networks during anesthesia—at nodal and global levels—were investigated using clustering coefficient, path length, and nodal efficiency measures. Results In all cortical regions, and in both wakeful and unconscious states (early and late), the genuine permutation cross mutual information and the percentage of genuine connections decreased with increasing distance, especially up to about 3 cm. The nodal cortical network metrics (the nodal clustering coefficients and nodal efficiency) decreased from wakefulness to unconscious state in the cortical regions we analyzed. In contrast, the global cortical network metrics slightly increased in the early unconscious state (the time span from loss of consciousness to 200 s after loss of consciousness), as compared with wakefulness (normalized average clustering coefficient: 1.05 ± 0.01 vs. 1.06 ± 0.03, P = 0.037; normalized average path length: 1.02 ± 0.01 vs. 1.04 ± 0.01, P = 0.021). Conclusions The genuine permutation cross mutual information reflected propofol-induced coupling changes measured at a cortical scale. Loss of consciousness was associated with a redistribution of the pattern of information integration; losing efficient global information transmission capacity but increasing local functional segregation in the cortical network. Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New

scholarly journals Triadic analysis of affiliation networks

2015 ◽  
Vol 3 (4) ◽  
pp. 480-508 ◽  
Author(s):  
JASON CORY BRUNSON
Keyword(s):  
Social Networks ◽  
Clustering Coefficient ◽  
Affiliation Networks ◽  
Instrumental Assessment ◽  
Triadic Closure ◽  
The One

AbstractTriadic closure has been conceptualized and measured in a variety of ways, most famously the clustering coefficient. Existing extensions to affiliation networks, however, are sensitive to repeat group attendance, which does not reflect common interpersonal interpretations of triadic closure. This paper proposes a measure of triadic closure in affiliation networks designed to control for this factor, which manifests in bipartite models as biclique proliferation. To avoid arbitrariness, the paper introduces a triadic framework for affiliation networks, within which a range of measures can be defined; it then presents a set of basic axioms that suffice to narrow this range to the one measure. An instrumental assessment compares the proposed and two existing measures for reliability, validity, redundancy, and practicality. All three measures then take part in an investigation of three empirical social networks, which illustrates their differences.

Weighted social networks for a large scale artificial society

2016 ◽  
Vol 30 (02) ◽  
pp. 1550276 ◽  
Author(s):  
Zong Chen Fan ◽  
Wei Duan ◽  
Peng Zhang ◽  
Xiao Gang Qiu
Keyword(s):  
Social Networks ◽  
Social Structure ◽  
Social Relationships ◽  
Large Scale ◽  
Census Data ◽  
Clustering Coefficient ◽  
Generation Model ◽  
Social Phenomenon ◽  
Artificial Society ◽  
Synthetic Population

The method of artificial society has provided a powerful way to study and explain how individual behaviors at micro level give rise to the emergence of global social phenomenon. It also creates the need for an appropriate representation of social structure which usually has a significant influence on human behaviors. It has been widely acknowledged that social networks are the main paradigm to describe social structure and reflect social relationships within a population. To generate social networks for a population of interest, considering physical distance and social distance among people, we propose a generation model of social networks for a large-scale artificial society based on human choice behavior theory under the principle of random utility maximization. As a premise, we first build an artificial society through constructing a synthetic population with a series of attributes in line with the statistical (census) data for Beijing. Then the generation model is applied to assign social relationships to each individual in the synthetic population. Compared with previous empirical findings, the results show that our model can reproduce the general characteristics of social networks, such as high clustering coefficient, significant community structure and small-world property. Our model can also be extended to a larger social micro-simulation as an input initial. It will facilitate to research and predict some social phenomenon or issues, for example, epidemic transition and rumor spreading.

Sign in / Sign up

Export Citation Format

Share Document