scholarly journals Network Replicability & Generalizability: Exploring the Effects of Sampling Variability, Scale Variability, and Node Reliability

2021 ◽  
Author(s):  
Arianne Constance Herrera-Bennett ◽  
Mijke Rhemtulla
Keyword(s):  
Network Models ◽  
Single Item ◽  
Sampling Variability ◽  
Network Characteristics ◽  
Network Sensitivity ◽  
Measurement Tools ◽  
Network Properties ◽  
The Common ◽  
Global Strength ◽  
Larger Sample

Work surrounding the replicability and generalizability of network models has increased in recent years, prompting debate as to whether network properties can be expected to be consistent across samples. To date, certain methodological practices may have contributed to observed inconsistencies, including the common use of single-item indicators to estimate nodes, and use of non-identical measurement tools. The current study used a resampling approach to systematically disentangle the effects of sampling variability from scale variability when assessing network replicability. Additionally, we explored the extent to which consistencies in network characteristics were improved when precision in node estimation was increased. Overall, scale variability produced less stability in network properties than sampling variability, however under more optimal measurement conditions (i.e. larger sample, greater node precision), discrepancies were markedly reduced. Findings also importantly underscored the value of improving node reliability: Use of multi-item indicators led to denser networks, higher network sensitivity, greater estimates of global strength, and greater levels of consistency in network properties (e.g., edge weights, centrality scores). Taken together, variability in network properties across samples may be less indicative of a lack of replicability, but may arise from poor measurement precision, and/or may reflect properties of the underlying true network model or scale-specific properties. All data and syntax are openly available online (https://osf.io/m37q2/).

Measuring the World

2018 ◽  
Author(s):  
Ann-Sophie Barwich
Keyword(s):  
Sensory Information ◽  
Network Models ◽  
Model System ◽  
Stimulus Input ◽  
Expectancy Effects ◽  
Neural Network Models ◽  
Perceptual Object ◽  
External Objects ◽  
The Common ◽  
The Brain

How much does stimulus input shape perception? The common-sense view is that our perceptions are representations of objects and their features and that the stimulus structures the perceptual object. The problem for this view concerns perceptual biases as responsible for distortions and the subjectivity of perceptual experience. These biases are increasingly studied as constitutive factors of brain processes in recent neuroscience. In neural network models the brain is said to cope with the plethora of sensory information by predicting stimulus regularities on the basis of previous experiences. Drawing on this development, this chapter analyses perceptions as processes. Looking at olfaction as a model system, it argues for the need to abandon a stimulus-centred perspective, where smells are thought of as stable percepts, computationally linked to external objects such as odorous molecules. Perception here is presented as a measure of changing signal ratios in an environment informed by expectancy effects from top-down processes.

scholarly journals Multi-model inference of network properties from incomplete data

2006 ◽  
Vol 3 (2) ◽  
pp. 123-136 ◽  
Author(s):  
Michael P. H. Stumpf ◽  
Thomas Thorne
Keyword(s):  
Network Models ◽  
Network Data ◽  
Global Networks ◽  
New Approach ◽  
Model Inference ◽  
Novel Approach ◽  
Eukaryotic Species ◽  
Network Properties ◽  
Experimental Approaches ◽  
Saccaromyces Cerevisiae

Summary It has previously been shown that subnets differ from global networks from which they are sampled for all but a very limited number of theoretical network models. These differences are of qualitative as well as quantitative nature, and the properties of subnets may be very different from the corresponding properties in the true, unobserved network. Here we propose a novel approach which allows us to infer aspects of the true network from incomplete network data in a multi-model inference framework. We develop the basic theoretical framework, including procedures for assessing confidence intervals of our estimates and evaluate the performance of this approach in simulation studies and against subnets drawn from the presently available PIN network data in Saccaromyces cerevisiae. We then illustrate the potential power of this new approach by estimating the number of interactions that will be detectable with present experimental approaches in sfour eukaryotic species, inlcuding humans. Encouragingly, where independent datasets are available we obtain consistent estimates from different partial protein interaction networks. We conclude with a discussion of the scope of this approaches and areas for further research

scholarly journals The use of Petri nets in the study of networked control systems

2019 ◽  
Author(s):  
S. Yuness ◽  
E.S. Lobusov
Keyword(s):  
Petri Nets ◽  
Control Systems ◽  
Communication Networks ◽  
Information Transfer ◽  
Network Performance ◽  
Networked Control Systems ◽  
Networked Control ◽  
Network Characteristics ◽  
Suspension Control ◽  
The Common

The use of communication networks in control systems has several important advantages, such as the ability of information transfer and remote control of various objects, the possibility of modifications and maintenance. On the other hand, the time between reading measurements from the sensor and sending a control signal to the actuator depends on the network characteristics (topology and routing scheme), and such a time delay can greatly affect the overall network performance. Delays, distortions and loss of transmitted data not only degrade the performance of the network management system, but also destabilize it. The paper considers the use of Petri nets as a method for modeling networked control systems (NCS) on the example of designing an active suspension control system for a car. When modeling, the star and common bus topologies were used, the comparison of which revealed that control systems with the common bus topology function 40% faster than systems with the star topology.

scholarly journals The Replicability and Generalizability of Internalizing Symptom Networks Across Five Samples

2019 ◽  
Author(s):  
Carter J. Funkhouser ◽  
Kelly Correa
Keyword(s):  
Network Structure ◽  
Internalizing Symptoms ◽  
Rank Order ◽  
Network Models ◽  
Clinical Samples ◽  
Cross Sectional ◽  
Total N ◽  
Correlation Networks ◽  
Global Strength ◽  
Central Symptom

The popularity of network analysis in psychopathology research has increased exponentially in recent years. Yet, little research has examined the replicability of cross-sectional psychopathology network models, and those that have used single items for symptoms rather than multi-item scales. The present study therefore examined the replicability and generalizability of regularized partial correlation networks of internalizing symptoms within and across five samples (total N = 2,573) using the Inventory for Depression and Anxiety Symptoms, a factor analytically-derived measure of individual internalizing symptoms. As different metrics may yield different conclusions about the replicability of network parameters, we examined both global and specific metrics of similarity between networks. Correlations within and between nonclinical samples suggested considerable global similarities in network structure (rss = .53-.87) and centrality strength (rss = .37-.86), but weaker similarities in network structure (rss = .36-.66) and centrality (rss = .04-.54) between clinical and nonclinical samples. Global strength (i.e., connectivity) did not significantly differ across all five networks and few edges (0-5.5%) significantly differed between networks. Specific metrics of similarity indicated that, on average, approximately 80% of edges were consistently estimated within and between all five samples. The most central symptom (i.e., dysphoria) was consistent within and across samples, but there were few other matches in centrality rank-order. In sum, there were considerable similarities in network structure, the presence and sign of individual edges, and the most central symptom within and across internalizing symptom networks estimated from nonclinical samples, but global metrics suggested network structure and symptom centrality had weak to moderate generalizability from nonclinical to clinical samples.

scholarly journals The dynamics of faculty hiring networks

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Eun Lee ◽  
Aaron Clauset ◽  
Daniel B. Larremore
Keyword(s):  
Network Models ◽  
Structural Mechanism ◽  
Faculty Hiring ◽  
Network Characteristics ◽  
Institutional Prestige ◽  
Structural Mechanisms ◽  
Global And Local ◽  
Global Placement ◽  
New Hire

AbstractFaculty hiring networks—who hires whose graduates as faculty—exhibit steep hierarchies, which can reinforce both social and epistemic inequalities in academia. Understanding the mechanisms driving these patterns would inform efforts to diversify the academy and shed new light on the role of hiring in shaping which scientific discoveries are made. Here, we investigate the degree to which structural mechanisms can explain hierarchy and other network characteristics observed in empirical faculty hiring networks. We study a family of adaptive rewiring network models, which reinforce institutional prestige within the hierarchy in five distinct ways. Each mechanism determines the probability that a new hire comes from a particular institution according to that institution’s prestige score, which is inferred from the hiring network’s existing structure. We find that structural inequalities and centrality patterns in real hiring networks are best reproduced by a mechanism of global placement power, in which a new hire is drawn from a particular institution in proportion to the number of previously drawn hires anywhere. On the other hand, network measures of biased visibility are better recapitulated by a mechanism of local placement power, in which a new hire is drawn from a particular institution in proportion to the number of its previous hires already present at the hiring institution. These contrasting results suggest that the underlying structural mechanism reinforcing hierarchies in faculty hiring networks is a mixture of global and local preference for institutional prestige. Under these dynamics, we show that each institution’s position in the hierarchy is remarkably stable, due to a dynamic competition that overwhelmingly favors more prestigious institutions. These results highlight the reinforcing effects of a prestige-based faculty hiring system, and the importance of understanding its ramifications on diversity and innovation in academia.

Spreading Activation Connectivity Based Approach to Network Clustering

2018 ◽  
pp. 207-219
Author(s):  
Alexander Troussov ◽  
Sergey Maruev ◽  
Sergey Vinogradov ◽  
Mikhail Zhizhin
Keyword(s):  
Big Data ◽  
Social Network ◽  
Social Systems ◽  
Network Models ◽  
Real Data ◽  
Spreading Activation ◽  
Network Clustering ◽  
Clustering Method ◽  
Network Characteristics ◽  
Structure Detection

Techno-social systems generate data, which are rather different, than data, traditionally studied in social network analysis and other fields. In massive social networks agents simultaneously participate in several contexts, in different communities. Network models of many real data from techno-social systems reflect various dimensionalities and rationales of actor's actions and interactions. The data are inherently multidimensional, where “everything is deeply intertwingled”. The multidimensional nature of Big Data and the emergence of typical network characteristics in Big Data, makes it reasonable to address the challenges of structure detection in network models, including a) development of novel methods for local overlapping clustering with outliers, b) with near linear performance, c) preferably combined with the computation of the structural importance of nodes. In this chapter the spreading connectivity based clustering method is introduced. The viability of the approach and its advantages are demonstrated on the data from the largest European social network VK.

scholarly journals Changes in network centrality of psychopathology symptoms between the COVID-19 outbreak and after peak

2020 ◽  
Vol 25 (12) ◽  
pp. 3140-3149
Author(s):  
Yuanyuan Wang ◽  
Zhishan Hu ◽  
Yi Feng ◽  
Amanda Wilson ◽  
Runsen Chen
Keyword(s):  
Motor Skills ◽  
Network Models ◽  
Anxiety And Depression ◽  
Psychological Interventions ◽  
Network Centrality ◽  
Patient Health ◽  
Network Properties ◽  
Psychopathology Symptoms ◽  
Psychomotor Symptoms ◽  
The Relationship

AbstractThe current study investigated the mechanism and changes in psychopathology symptoms throughout the COVID-19 outbreak and after peak. Two studies were conducted separately in China during outbreak and the after peak stages, with 2540 participants were recruited from February 6 to 16, 2020, and 2543 participants were recruited from April 25 to May 5, 2020. The network models were created to explore the relationship between psychopathology symptoms both within and across anxiety and depression, with anxiety measured by the Generalized Anxiety Disorder-7 and depression measured by the Patient Health Questionnaire-9. Symptom network analysis was conducted to evaluate network and bridge centrality, and the network properties were compared between the outbreak and after peak. Noticeably, psychomotor symptoms such as impaired motor skills, restlessness, and inability to relax exhibited high centrality during the outbreak, which still relatively high but showed substantial remission during after peak stage (in terms of strength, betweenness, or bridge centrality). Meanwhile, symptoms of irritability (strength, betweenness, or bridge centrality) and loss of energy (bridge centrality) played an important role in the network after the peak of the pandemic. This study provides novel insights into the changes in central features during the different COVID-19 stages and highlights motor-related symptoms as bridge symptoms, which could activate the connection between anxiety and depression. The results revealed that restrictions on movement were associated with worsen in psychomotor symptoms, indicating that future psychological interventions should target motor-related symptoms as priority.

scholarly journals National Airline Networks : A Graph Theoretic Analysis

2005 ◽  
Vol 19 (48) ◽  
pp. 461-474
Author(s):  
Siim Sööt
Keyword(s):  
Regression Analyses ◽  
Theoretic Analysis ◽  
Analytic Framework ◽  
Causal Factors ◽  
Network Characteristics ◽  
Graph Theoretic ◽  
Dependent Variables ◽  
Network Properties ◽  
Domestic Airline ◽  
Topographic Relief

Network properties of national domestic airline Systems are examined and linked to causal factors such as levels of economic development, population size and distribution, topographic relief, and size of country. Graph theoretic indices are utilized to measure network characteristics and become the independant variables in regression analyses. The theoretical pitfalls of this method are highlighted by utilizing a path analytic framework to identify the degree of interrelationship among the dependent variables. Still, the graph theoretic method is deemed useful as a means of topologic analysis of network structures.

Association of cfDNA androgen receptor amplifications with BRAF alterations in mCRPC.

2017 ◽  
Vol 35 (6_suppl) ◽  
pp. 234-234
Author(s):  
Elisa M. Ledet ◽  
Joshua Schiff ◽  
Patrick Cotogno ◽  
Charlotte Manogue ◽  
Emma M. Ernst ◽  
...  
Keyword(s):  
Point Mutations ◽  
Response To Therapy ◽  
Cancer Center ◽  
Therapeutic Implications ◽  
Heterogenous Group ◽  
Somatic Alterations ◽  
The Common ◽  
The Relationship ◽  
Larger Sample ◽  
Redwood City

234 Background: Cell free DNA (cfDNA) present in plasma of cancer pts can reflect tumoral alterations. Genomic alterations in cfDNA alter prognosis and abiraterone/enzalutamide resistance in mCRPC. The goal of this evaluation was to characterize AR amplifications (Amps) and various somatic point mutations (Muts) detected in mCRPC cfDNA and to relate those changes to other common alterations in the cfDNA landscape. Methods: A heterogenous group of 46 mCRPC patients (pts) with evidence of clinical progression from Tulane Cancer Center underwent cfDNA analysis using Guardant360 test (Guardant Health, Redwood City, CA). This evaluation included full exonic coverage of 70 genes and amplifications in 18 genes. Mutations reported herein include both known pathogenic mutations as well as mutations uncharacterized for functional importance. Results: 69.5% (n = 32) of the mCRPC pts evaluated had an AR alteration. Of the pts with AR alterations, 46.8% (n = 16) had AR Amps, 43.7% (n = 14) had AR Muts, and only 6.25% (n = 2) had both. In this cohort, AR alterations were the most commonly observed aberration. In addition to amplifications, 12 different AR Muts were detected. AR Muts included: T878A (n = 9), H875Y (n = 5), W742C (n = 4), AR L702H (n = 3), and others. To better understand the relationship between AR alteration and other commonly detected cfDNA aberrations, association between BRAF (35.5%), TP53 (46.7%), and MYC (22.2%) alterations and AR were assessed. Among these genes, TP53 alterations were all Muts and MYC alterations were all Amps. BRAF alterations were predominantly Amps (N = 15) though Muts were also detected (N = 6). Neither TP53 Muts or MYC Amps were significantly associated with AR alterations. On the other hand, BRAF alterations were significantly associated with AR Amps (p = 0.041); 60% (9/15) pts with AR Amps also had BRAF alteration (Odds ratio = 7.71, 95% CI 1.284- 46.366). Conclusions: AR alterations in cfDNA impact both disease progression and response to therapy. Co-segregation of AR and BRAF alterations may have significant prognostic and therapeutic implications. Further research and larger sample size is needed to further elucidate associations between the common somatic alterations detected in mCRPC.

Sensitivity Analysis of the Networks with Fractional Order Reactance

2013 ◽  
Vol 860-863 ◽  
pp. 2309-2314
Author(s):  
Gui Shu Liang ◽  
Xing Hua Zheng ◽  
Long Ma ◽  
Hua Ying Dong
Keyword(s):  
Sensitivity Analysis ◽  
Fractional Calculus ◽  
Fractional Order ◽  
Network Theory ◽  
Network Models ◽  
Element Model ◽  
Network Sensitivity

Fractional calculus theory has gained more and more applications in numerous fields. In many cases, using fractional reactance element model can describe the properties of objects more accurately and simply. This paper studies the sensitivity of networks with fractional order reactance, puts forward the adjoint network sensitivity formulas and the incremental network models of fractional order capacitor and inductor, which will further develop the adjoint network and Incremental network theory. The simulation verification is also given.

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