Relevant bibliographies by topics / Data Network Effects

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Data Network Effects'

Author: Grafiati
Published: 29 June 2021
Last updated: 3 February 2022

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Journal articles on the topic "Data Network Effects"

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Smith, Jeffrey A., and G. Robin Gauthier. "Estimating Contextual Effects from Ego Network Data." Sociological Methodology 50, no. 1 (June 2, 2020): 215–75. http://dx.doi.org/10.1177/0081175020922879.

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Network concepts are often used to characterize the features of a social context. For example, past work has asked if individuals in more socially cohesive neighborhoods have better mental health outcomes. Despite the ubiquity of use, it is relatively rare for contextual studies to use the methods of network analysis. This is the case, in part, because network data are difficult to collect, requiring information on all ties between all actors. In this article the authors ask whether it is possible to avoid such heavy data collection while still retaining the best features of a contextual-network study. The basic idea is to apply network sampling to the problem of contextual models, in which one uses sampled ego network data to infer the network features of each context and then uses the inferred network features as second-level predictors in a hierarchical linear model. The authors test the validity of this idea in the case of network cohesion. Using two complete data sets as a test, the authors find that ego network data are sufficient to capture the relationship between cohesion and important outcomes, such as attachment and deviance. The hope, going forward, is that researchers will find it easier to incorporate holistic network measures into traditional regression models.
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De Giorgi, Giacomo, Anders Frederiksen, and Luigi Pistaferri. "Consumption Network Effects." Review of Economic Studies 87, no. 1 (May 6, 2019): 130–63. http://dx.doi.org/10.1093/restud/rdz026.

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Abstract In this article we study consumption network effects. Does the consumption of our peers affect our own consumption? How large is such effect? What are the economic mechanisms behind it? We use administrative panel data on Danish households to construct a measure of consumption based on tax records on income and assets. We combine tax record data with matched employer–employee data to identify peer groups based on workplace, which gives us a much tighter and credible definition of networks than used in previous literature. We use the non-overlapping network structure of one’s peers group, as well as firm-level shocks, to build valid instruments for peer consumption. We estimate non-negligible and statistically significant network effects, capable of generating sizable multiplier effect at the macro-level. We also investigate what mechanisms generate such effects, distinguishing between intertemporal and intratemporal consumption effects as well as a more traditional risk sharing view.
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Jochmans, Koen, and Martin Weidner. "Fixed‐Effect Regressions on Network Data." Econometrica 87, no. 5 (2019): 1543–60. http://dx.doi.org/10.3982/ecta14605.

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This paper considers inference on fixed effects in a linear regression model estimated from network data. An important special case of our setup is the two‐way regression model. This is a workhorse technique in the analysis of matched data sets, such as employer–employee or student–teacher panel data. We formalize how the structure of the network affects the accuracy with which the fixed effects can be estimated. This allows us to derive sufficient conditions on the network for consistent estimation and asymptotically valid inference to be possible. Estimation of moments is also considered. We allow for general networks and our setup covers both the dense and the sparse case. We provide numerical results for the estimation of teacher value‐added models and regressions with occupational dummies.
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Sewell, Daniel K. "Latent space models for network perception data." Network Science 7, no. 2 (April 15, 2019): 160–79. http://dx.doi.org/10.1017/nws.2019.1.

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AbstractSocial networks, wherein the edges represent nonbehavioral relations such as friendship, power, and influence, can be difficult to measure and model. A powerful tool to address this is cognitive social structures (Krackhardt, D. (1987). Cognitive social structures. Social Networks, 9(2), 109–134.), where the perception of the entire network is elicited from each actor. We provide a formal statistical framework to analyze informants’ perceptions of the network, implementing a latent space network model that can estimate, e.g., homophilic effects while accounting for informant error. Our model allows researchers to better understand why respondents’ perceptions differ. We also describe how to construct a meaningful single aggregated network that ameliorates potential respondent error. The proposed method provides a visualization method, an estimate of the informants’ biases and variances, and we describe a method for sidestepping forced-choice designs.
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Welles, Brooke Foucault, and Noshir Contractor. "Individual Motivations and Network Effects." ANNALS of the American Academy of Political and Social Science 659, no. 1 (April 9, 2015): 180–90. http://dx.doi.org/10.1177/0002716214565755.

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This article explores the relative influence of individual and network-level effects on the emergence of online social relationships. Using network modeling and data drawn from logs of social behavior inside the virtual world Second Life, we combine individual- and network-level theories into an integrated model of online social relationship formation. Results reveal that time spent online and the network pressure toward balance (individuals tending to form relationships with others who have relationships in common) predict the emergence of online relationship ties, while gender, age, proximity, homophily (the tendency of individuals to form relationships among people with similar traits), and preferential attachment are not significant predictors within the observed networks. We discuss these results in light of existing research on online social relationships and describe how digital data and network analytics enable novel insights about the emergence of online social relationships.
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Khamis, Azme, Zuhaimy Ismail ., Khalid Haron ., and Ahmad Tarmizi Mohamm . "The Effects of Outliers Data on Neural Network Performance." Journal of Applied Sciences 5, no. 8 (July 15, 2005): 1394–98. http://dx.doi.org/10.3923/jas.2005.1394.1398.

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Rosin, Paul L., and Freddy Fierens. "The effects of data filtering on neural network learning." Neurocomputing 20, no. 1-3 (August 1998): 155–62. http://dx.doi.org/10.1016/s0925-2312(98)00008-3.

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Maharjan, Pragya. "Effects of Social Network on Health Education." Shiksha Shastra Saurabh 21 (December 31, 2018): 31–36. http://dx.doi.org/10.3126/sss.v21i0.35087.

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Social networks have become an inseparable part of our modern life. Social network, if used properly, can be very beneficial for all the users in every respect. There are many researched applications of Social network in different fields, its application in Health Education was not mentioned anywhere properly, though. For the survey, the students of Bachelors’ levels, majoring in Health and Physical Education in constituent and affiliated colleges of T.U. of Kathmandu Valley were selected from the different colleges. Altogether 171 students, who were acquainted with Social network, were selected as respondents for the study. Questionnaires were used to collect quantitative data and interview guidelines were used to obtain qualitative data from teachers. The students and teachers profoundly used Social network still they should use it for educational purpose. The communication gap between teachers and students were clearly perceived.
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Oană, Iulian. "Network Effects on Rhythms of Scientific Publications." International Review of Social Research 8, no. 2 (December 1, 2018): 143–55. http://dx.doi.org/10.2478/irsr-2018-0016.

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Abstract Studies based on bibliometric records have introduced the idea of ‘rhythmicity’ when it comes to the publication of research articles. However, the main approach of this particular topic was to analyze journal specific data on rates of manuscript and review submissions. This study takes another path, by analyzing aspects of publication rhythmicity based not on individual, attribute data, but taking into account the fact that publication of research results and the efforts leading to a certain manuscript are often collective endeavors. Thus, co-authorship ego networks are interpreted through the theoretical lenses of ‘social time’ (for temporality), and ‘homophily’ and ‘preferential attachment’ (for network characteristics). For this article, the same data analyzed by M.-G. Hâncean and M. Perc in their 2016 article, Homophily in coauthorship networks of East European sociologists, were used. The data was based on Web of Science bibliometric records for three populations of academic sociologists, from Poland, Romania and Slovenia, and their co-authors. The purpose was to see if the publishing rhythm of an author (i.e., ego) is influenced by the publishing rhythm of her co-authors (i.e., alters) and by the structural characteristics of her ego-network. Rhythmicity was measured as the sum of standard deviations from the mean for the number of articles published between 2006 and 2016, resulting in a score which characterizes egos and alters as constant or irregular in their publishing activity. Results suggest that the structural features of the co-authorship networks can give us certain insights for the rhythmicity of publications. Mainly, structural features of network size, density and node betweenness explain more the variation of egos’ constancy or irregularity in (non)publication than the rhythmicity of their co-authors.
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McPherson, Miller, and Jeffrey A. Smith. "Network Effects in Blau Space: Imputing Social Context from Survey Data." Socius: Sociological Research for a Dynamic World 5 (January 2019): 237802311986859. http://dx.doi.org/10.1177/2378023119868591.

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We develop a method of imputing ego network characteristics for respondents in probability samples of individuals. This imputed network uses the homophily principle to estimate certain properties of a respondent’s core discussion network in the absence of actual network data. These properties measure the potential exposure of respondents to the attitudes, values, beliefs, and so on of their (likely) network alters. We use American National Election Study data to demonstrate that the imputed network features show substantial effects on individual-level measures, such as political attitudes and beliefs. In some cases, the imputed network variable substantially reduces the effects of standard sociodemographic variables, like age and education. We argue that the imputed network variable captures many of the aspects of social context that have been at the core of sociological analysis for decades.
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Dissertations / Theses on the topic "Data Network Effects"

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Örblom, Markus. "Effects of Network Performance on Smartphone User Behavior." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-177547.

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While the relation between smartphone user behavior and contextual factors have been explored in previous research, the mobile networks’ influence on the smartphone user behavior is largely unknown. Through statistical analysis of a data set collected globally from ˜ 1000 users by an Android app called Ericsson Apps, this study investigates how the users’ app choices and app usage depend on the network performance. The results show, for instance, that the choice of app depends strongly on the network performance, suggesting that it is a factor in the users’ app choices. For example, Swedish users are ˜ 3 times more likely to use Facebook on LTE than when disconnected, i.e., no access to the mobile networks, while ˜ 4.6 times more likely to make a phone call when disconnectedthan on LTE. Additionally, the data analysis finds a demand for better performance in the mobile networks as the downlink data consumption grows linearly without decline, with respect to network performance, for media and video types of apps.
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Sathyanarayana, Supreeth. "Characterizing the effects of device components on network traffic." Thesis, Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/47640.

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When a network packet is formed by a computer's protocol stack, there are many components (e.g., Memory, CPU, etc.) of the computer that are involved in the process. The objective of this research is to identify, characterize and analyze the effects of the various components of a device (e.g., Memory, CPU, etc.) on the device's network traffic by measuring the changes in its network traffic with changes in its components. We also show how this characterization can be used to effectively perform counterfeit detection of devices which have counterfeit components (e.g., Memory, CPU, etc.). To obtain this characterization, we measure and apply statistical analyses like probability distribution fucntions (PDFs) on the interarrival times (IATs) of the device's network packets (e.g., ICMP, UDP, TCP, etc.). The device is then modified by changing just one component (e.g., Memory, CPU, etc.) at a time while holding the rest constant and acquiring the IATs again. This, over many such iterations provides an understanding of the effect of each component on the overall device IAT statistics. Such statistics are captured for devices (e.g., field-programmable gate arrays (FPGAs) and personal computers (PCs)) of different types. Some of these statistics remain stable across different IAT captures for the same device and differ for different devices (completely different devices or even the same device with its components changed). Hence, these statistical variations can be used to detect changes in a device's composition, which lends itself well to counterfeit detection. Counterfeit devices are abundant in today's world and cause billions of dollars of loss in revenue. Device components are substituted with inferior quality components or are replaced by lower capacity components. Armed with our understanding of the effects of various device components on the device's network traffic, we show how such substitutions or alterations of legitimate device components can be detected and hence perform effective counterfeit detection by statistically analyzing the deviation of the device's IATs from that of the original legitimate device. We perform such counterfeit detection experiments on various types of device configurations (e.g., PC with changed CPU, RAM, etc.) to prove the technique's efficacy. Since this technique is a fully network-based solution, it is also a non-destructive technique which can quickly, inexpensively and easily verify the device's legitimacy. This research also discusses the limitations of network-based counterfeit detection.
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Merchán, Dueñas Daniel Esteban. "Effects of road-network circuity on strategic decisions in urban logistics." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119911.

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Thesis: Ph. D. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2018.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 114-120).
This thesis proposes a research framework that leverages high-resolution traffic and urban infrastructure data to improve analytical approximation methods used to inform strategic decisions in designing last-mile distribution systems. In particular, this thesis explores the effects of the road-network on the circuity of local trips, and introduces data-driven extensions to improve predictive performance of route distance approximation methods by increasing the resolution of the underlying urban road-network. Overall, these circuity-based extensions significantly increase the real-world validity of routing approximations compared to classical methods, and entail relevant implications in the configuration of logistics networks within urban markets. The framework presented in this thesis entails three inter-dependent levels of analysis: individual trip, consolidated route and last-mile network levels. In Chapter 2, we introduce a method to quantify and analyze the network circuity of local trips leveraging contemporary traffic datasets. Using the city of Sao Paulo as the primary illustrative example and a combination of supervised and unsupervised machine learning methods, significant heterogeneities in local network circuity are observed, explained by dimensional and topological properties of the road-network. Results from Sao Paulo are compared to seven additional large and medium-sized urban areas in Latin America and the United States. At a coarse-grained level of analysis, we observe similar correlations between road-network properties and local circuity across these cities. In Chapter 3, this thesis proposes a data-driven extension to continuum approximation-based methods used to predict urban route distances. This extension efficiently incorporates the circuity of the underlying road-network into the approximation method to improve distance predictions in more realistic settings. The proposed extension significantly outperforms classic methods, which build on the assumption of travel according to the rectilinear distance metric within urban areas. By only marginally increasing the data collection effort, results of the proposed extension yield error reductions between 20-30% in mean absolute percentage error compared to classical approximation methods and are within 10 - 20% compared to near-optimal solutions obtained with a local search heuristic. Further, by providing a real-world validation of classic continuum approximation-based methods, we explore how contemporary mapping technologies and novel sources of geo-spatial and traffic data can be efficiently leveraged to improve the predictive performance of these methods. Finally, building on the augmented route distance approximation, in Chapter 4 we explore the effect of road-network circuity on the design and planning of urban last-mile distribution systems. These improved routing approximations are used within an integer linear programming model to solve large-scale, real-world instances of the two-echelon capacitated location routing problem. Using the parcel delivery operation of Brazil's largest e-commerce platform in the city of Sao Paulo as the primary example to illustrate the impact and relevance of this work, we demonstrate how explicitly accounting for local variations in road-network circuity can yield relevant implications for fleet capacity planning, the location of urban distribution facilities, and the definition of facility-specific service areas. Results indicate that failing to account for local circuity would underestimate the necessary fleet size by 20% and would increase the total last-mile network cost by approximately 8%.
by Daniel Esteban Merchán Dueñas.
Ph. D. in Engineering Systems
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Vuyyuru, Sisir. "Data Collection Network and Data Analysis for the Prototype Local Area Augmentation System Ground Facility." Ohio University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1195158113.

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Raoufi-Danner, Torrin. "Effects of Missing Values on Neural Network Survival Time Prediction." Thesis, Linköpings universitet, Statistik och maskininlärning, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-150339.

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Data sets with missing values are a pervasive problem within medical research. Building lifetime prediction models based solely upon complete-case data can bias the results, so imputation is preferred over listwise deletion. In this thesis, artificial neural networks (ANNs) are used as a prediction model on simulated data with which to compare various imputation approaches. The construction and optimization of ANNs is discussed in detail, and some guidelines are presented for activation functions, number of hidden layers and other tunable parameters. For the simulated data, binary lifetime prediction at five years was examined. The ANNs here performed best with tanh activation, binary cross-entropy loss with softmax output and three hidden layers of between 15 and 25 nodes. The imputation methods examined are random, mean, missing forest, multivariate imputation by chained equations (MICE), pooled MICE with imputed target and pooled MICE with non-imputed target. Random and mean imputation performed poorly compared to the others and were used as a baseline comparison case. The other algorithms all performed well up to 50% missingness. There were no statistical differences between these methods below 30% missingness, however missing forest had the best performance above this amount. It is therefore the recommendation of this thesis that the missing forest algorithm is used to impute missing data when constructing ANNs to predict breast cancer patient survival at the five-year mark.
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Fadul, Waad. "Data-Driven Health Services: an Empirical Investigation on the Role of Artificial Intelligence and Data Network Effects in Value Creation." Thesis, Uppsala universitet, Informationssystem, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447507.

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The purpose of this study is to produce new knowledge concerning the perceived user’s value generated using machine learning technologies that activate data network effects factors that create value through various business model themes. The data network effects theory represents a set of factors that increase the user’s perceived value for a platform that uses artificial intelligence capabilities. The study followed an abductive research approach where initially found facts were matched against the data network effects theory to be put in context and understood. The study’s data was gathered through semi-structured interviews with experts who were active within the research area and chosen based on their practical experience and their role in the digitization of the healthcare sector. The results show that three out of six factors were fully realized contributing to value creation while two of the factors showed to be partially realized in order to contribute to value creation and that is justified by the exclusion of users' perspectives in the scope of the research. Lastly, only one factor has limited contribution to the value creation due to the heavy regulations limiting its realization in the health sector. It is concluded that data network effects moderators contributed differently in the activation of various business model themes for value creation in a general manner where further studies should apply the theory in the assessment of one specific AI health offering to take full advantage of the theory potential. The theoretical implications showed that the data network factors may not necessarily be equally activated to contribute to value creation which was not initially highlighted by the theory. Additionally, the practical implications of the study’s results may help managers in their decision-making process on which factors to be activated for which business model theme.
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Dayton, Jonathan Bryan. "Adversarial Deep Neural Networks Effectively Remove Nonlinear Batch Effects from Gene-Expression Data." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7521.

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Gene-expression profiling enables researchers to quantify transcription levels in cells, thus providing insight into functional mechanisms of diseases and other biological processes. However, because of the high dimensionality of these data and the sensitivity of measuring equipment, expression data often contains unwanted confounding effects that can skew analysis. For example, collecting data in multiple runs causes nontrivial differences in the data (known as batch effects), known covariates that are not of interest to the study may have strong effects, and there may be large systemic effects when integrating multiple expression datasets. Additionally, many of these confounding effects represent higher-order interactions that may not be removable using existing techniques that identify linear patterns. We created Confounded to remove these effects from expression data. Confounded is an adversarial variational autoencoder that removes confounding effects while minimizing the amount of change to the input data. We tested the model on artificially constructed data and commonly used gene expression datasets and compared against other common batch adjustment algorithms. We also applied the model to remove cancer-type-specific signal from a pan-cancer expression dataset. Our software is publicly available at https://github.com/jdayton3/Confounded.
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Larsson, Marcus, and Christoffer Möckelind. "The effects of Deep Belief Network pre-training of a Multilayered perceptron under varied labeled data conditions." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-187374.

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Sometimes finding labeled data for machine learning tasks is difficult. This is a problem for purely supervised models like the Multilayered perceptron(MLP). A Discriminative Deep Belief Network(DDBN) is a semi-supervised model that is able to use both labeled and unlabeled data. This research aimed to move towards a rule of thumb of when it is beneficial to use a DDBN instead of an MLP, given the proportions of labeled and unlabeled data. Several trials with different amount of labels, from the MNIST and Rectangles-Images datasets, were conducted to compare the two models. It was found that for these datasets, the DDBNs had better accuracy when few labels were available. With 50% or more labels available, the DDBNs and MLPs had comparable accuracies. It is concluded that a rule of thumb of using a DDBN when less than 50% of labels are available for training, would be in line with the results. However, more research is needed to make any general conclusions.
Märkt data kan ibland vara svårt att hitta för maskininlärningsuppgifter. Detta är ett problem för modeller som bygger på övervakad inlärning, exem- pelvis Multilayerd Perceptron(MLP). Ett Discriminative Deep Belief Network (DDBN) är en semi-övervakad modell som kan använda både märkt och omärkt data. Denna forskning syftar till att närma sig en tumregel om när det är för- delaktigt att använda en DDBN i stället för en MLP, vid olika proportioner av märkt och omärkt data. Flera försök med olika mängd märkt data, från MNIST och Rectangle-Images datamängderna, genomfördes för att jämföra de två mo- dellerna. Det konstaterades att för dessa datamängder hade DDBNerna bättre precision när ett fåtal märkt data fanns tillgängligt. När 50% eller mer av datan var märkt, hade DDBNerna och MLPerna jämförbar noggrannhet. Slutsatsen är att en tumregel att använda en DDBN när mindre än 50% av av träningsdatan är märkt, skulle vara i linje med resultaten. Det behövs dock mer forskning för att göra några generella slutsatser.
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Diaz, Boada Juan Sebastian. "Polypharmacy Side Effect Prediction with Graph Convolutional Neural Network based on Heterogeneous Structural and Biological Data." Thesis, KTH, Numerisk analys, NA, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-288537.

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The prediction of polypharmacy side effects is crucial to reduce the mortality and morbidity of patients suffering from complex diseases. However, its experimental prediction is unfeasible due to the many possible drug combinations, leaving in silico tools as the most promising way of addressing this problem. This thesis improves the performance and robustness of a state-of-the-art graph convolutional network designed to predict polypharmacy side effects, by feeding it with complexity properties of the drug-protein network. The modifications also involve the creation of a direct pipeline to reproduce the results and test it with different datasets.
För att minska dödligheten och sjukligheten hos patienter som lider av komplexa sjukdomar är det avgörande att kunna förutsäga biverkningar från polyfarmaci. Att experimentellt förutsäga biverkningarna är dock ogenomförbart på grund av det stora antalet möjliga läkemedelskombinationer, vilket lämnar in silico-verktyg som det mest lovande sättet att lösa detta problem. Detta arbete förbättrar prestandan och robustheten av ett av det senaste grafiska faltningsnätverken som är utformat för att förutsäga biverkningar från polyfarmaci, genom att mata det med läkemedel-protein-nätverkets komplexitetsegenskaper. Ändringarna involverar också skapandet av en direkt pipeline för att återge resultaten och testa den med olika dataset.
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McMorries, David W. "Investigation into the effects of voice and data convergence on a Marine Expeditionary Bridgade TRI-TAC digital transmission network." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2000. http://handle.dtic.mil/100.2/ADA379684.

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Thesis (M.S. in Information Technology Management)--Naval Postgraduate School, June 2000.
Thesis advisors, Osmundson, John S. ; Brady, Terrence C. "June 2000." Includes bibliographical references (p. 69). Also available in print.
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Books on the topic "Data Network Effects"

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McMorries, David W. Investigation into the effects of voice and data convergence on a Marine Expeditionary Bridgade TRI-TAC digital transmission network. Monterey, Calif: Naval Postgraduate School, 2000.

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Information systems for global financial markets: Emerging developments and effects. Hershey, PA: Business Science Reference, 2012.

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Varlamov, Oleg. Mivar databases and rules. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1508665.

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The multidimensional open epistemological active network MOGAN is the basis for the transition to a qualitatively new level of creating logical artificial intelligence. Mivar databases and rules became the foundation for the creation of MOGAN. The results of the analysis and generalization of data representation structures of various data models are presented: from relational to "Entity — Relationship" (ER-model). On the basis of this generalization, a new model of data and rules is created: the mivar information space "Thing-Property-Relation". The logic-computational processing of data in this new model of data and rules is shown, which has linear computational complexity relative to the number of rules. MOGAN is a development of Rule - Based Systems and allows you to quickly and easily design algorithms and work with logical reasoning in the "If..., Then..." format. An example of creating a mivar expert system for solving problems in the model area "Geometry"is given. Mivar databases and rules can be used to model cause-and-effect relationships in different subject areas and to create knowledge bases of new-generation applied artificial intelligence systems and real-time mivar expert systems with the transition to"Big Knowledge". The textbook in the field of training "Computer Science and Computer Engineering" is intended for students, bachelors, undergraduates, postgraduates studying artificial intelligence methods used in information processing and management systems, as well as for users and specialists who create mivar knowledge models, expert systems, automated control systems and decision support systems. Keywords: cybernetics, artificial intelligence, mivar, mivar networks, databases, data models, expert system, intelligent systems, multidimensional open epistemological active network, MOGAN, MIPRA, KESMI, Wi!Mi, Razumator, knowledge bases, knowledge graphs, knowledge networks, Big knowledge, products, logical inference, decision support systems, decision-making systems, autonomous robots, recommendation systems, universal knowledge tools, expert system designers, logical artificial intelligence.
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Internet discourse and health debates. Basingstoke, Hampshire: Palgrave Macmillan, 2005.

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P, Masterson John, Johnson Carole D, Climate and Land Use Change Research Development Program (U.S.), and Geological Survey (U.S.), eds. Well network installation and hydrogeologic data collection, Assateague Island National Seashore, Worcester County, Maryland, 2010. Reston, Va: U.S. Dept. of the Interior, U.S. Geological Survey, 2012.

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Kerr, Kerri A. Quaker Valley Digital School District: Early effects and plans for future evaluation. Santa Monica, CA: Rand, 2003.

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International Conference on Systems Research, Informatics, and Cybernetics (19th 2007 Baden-Baden, Germany). Advances in environmental systems research: Sustainability, environmental sciences, support systems : effects of electromagnetic exposition on honeybees, principles of neuro-empirism and dynamic models, application of stochastic networks, sustainability of fuzzy theory, object oriented analysis, integrated logistic support principles, business information management system, sustainable decision support systems, health service delivery. Tecumseh, Ont: International Institute for Advanced Studies in Systems Research and Cybernetics, 2007.

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International Conference on Systems Research, Informatics, and Cybernetics (19th 2007 Baden-Baden, Germany). Advances in environmental systems research: Sustainability, environmental sciences, support systems : effects of electromagnetic exposition on honeybees, principles of neuro-empirism and dynamic models, application of stochastic networks, sustainability of fuzzy theory, object oriented analysis, integrated logistic support principles, business information management system, sustainable decision support systems, health service delivery. Tecumseh, Ont: International Institute for Advanced Studies in Systems Research and Cybernetics, 2007.

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Stefanie, Lindstaedt, Kloos Carlos Delgado, Hernández-Leo Davinia, and SpringerLink (Online service), eds. 21st Century Learning for 21st Century Skills: 7th European Conference of Technology Enhanced Learning, EC-TEL 2012, Saarbrücken, Germany, September 18-21, 2012. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Ontario Educational Research Council. Conference. [Papers presented at the 36th Annual Conference of the Ontario Educational Research Council, Toronto, Ontario, December 2-3, 1994]. [Toronto, ON: s.n.], 1994.

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Book chapters on the topic "Data Network Effects"

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De Marsico, Maria, Luca Moschella, Andrea Sterbini, and Marco Temperini. "Effects of Network Topology on the OpenAnswer’s Bayesian Model of Peer Assessment." In Data Driven Approaches in Digital Education, 385–90. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66610-5_31.

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Park, Kyungseo, and Ramez Elmasri. "Effects of Storage Architecture on Performance of Sensor Network Queries." In Information Networking. Advances in Data Communications and Wireless Networks, 247–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11919568_25.

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Akhmet, Marat, Duygu Aruğaslan Çinçin, and Nur Cengiz. "Dynamics of a Recurrent Neural Network with Impulsive Effects and Piecewise Constant Argument." In Trends in Data Engineering Methods for Intelligent Systems, 540–52. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79357-9_52.

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Jung, Joon-Young, and Jae-Min Ahn. "Effects of the Distinction between Long and Short Data Grants in DOCSIS Network." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 310–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-11284-3_32.

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Heinkelmann, Robert, Kyriakos Balidakis, Apurva Phogat, Cuixian Lu, Julian A. Mora-Diaz, Tobias Nilsson, and Harald Schuh. "Effects of Meteorological Input Data on the VLBI Station Coordinates, Network Scale, and EOP." In International Symposium on Earth and Environmental Sciences for Future Generations, 195–202. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/1345_2016_243.

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Zörgő, Szilvia, Zachari Swiecki, and A. R. Ruis. "Exploring the Effects of Segmentation on Semi-structured Interview Data with Epistemic Network Analysis." In Communications in Computer and Information Science, 78–90. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67788-6_6.

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McGarry, Ken, and Ennock Assamoha. "Data Integration with Self-organising Neural Network Reveals Chemical Structure and Therapeutic Effects of Drug ATC Codes." In Advances in Intelligent Systems and Computing, 63–74. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66939-7_6.

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Banhatti, Aniruddha Gopal, and Paresh Chandra Deka. "Effects of Data Pre-processing on the Prediction Accuracy of Artificial Neural Network Model in Hydrological Time Series." In Urban Hydrology, Watershed Management and Socio-Economic Aspects, 265–75. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40195-9_21.

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Zwitter, Andrej. "The Network Effect on Ethics in the Big Data Age." In Big Data Challenges, 23–34. London: Palgrave Macmillan UK, 2016. http://dx.doi.org/10.1057/978-1-349-94885-7_3.

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Kocak, Fatih, George Kesidis, and Serge Fdida. "Network Neutrality with Content Caching and Its Effect on Access Pricing." In Smart Data Pricing, 47–66. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118899250.ch3.

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Conference papers on the topic "Data Network Effects"

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Saveski, Martin, Jean Pouget-Abadie, Guillaume Saint-Jacques, Weitao Duan, Souvik Ghosh, Ya Xu, and Edoardo M. Airoldi. "Detecting Network Effects." In KDD '17: The 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3097983.3098192.

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Lin, Anne, Andrees Abeliuk, and Emilio Ferrara. "Effects of Network Structure on Subjective Preference Diversity." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9005454.

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Arbour, David, Dan Garant, and David Jensen. "Inferring Network Effects from Observational Data." In KDD '16: The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2939672.2939791.

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Christodoulakis, Christina, Christos Faloutsos, and Renee J. Miller. "VoidWiz: Resolving incompleteness using network effects." In 2014 IEEE 30th International Conference on Data Engineering (ICDE). IEEE, 2014. http://dx.doi.org/10.1109/icde.2014.6816748.

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Sun, Yanan, Peiqin Zhang, and Jinhua Fei. "CEO Turnover, Network Effects, and Firm Performance." In 2020 5th IEEE International Conference on Big Data Analytics (ICBDA). IEEE, 2020. http://dx.doi.org/10.1109/icbda49040.2020.9101323.

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Ozcan, Selim, and Ahmet Fatih Mustacoglu. "Transfer Learning Effects on Image Steganalysis with Pre-Trained Deep Residual Neural Network Model." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622437.

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Abdullah, Kareem, Noha Korany, Ayman Khalafallah, Ahmed Saeed, and Ayman Gaber. "Characterizing the Effects of Rapid LTE Deployment: A Data-Driven Analysis." In 2019 Network Traffic Measurement and Analysis Conference (TMA). IEEE, 2019. http://dx.doi.org/10.23919/tma.2019.8784522.

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Mase, Hajime, Maria T. Reis, Shunji Nagahashi, Takehisa Saitoh, and Terry S. Hedges. "EFFECTS OF ZERO-OVERTOPPING DATA IN ARTIFICIAL NEURAL NETWORK PREDICTIONS." In Proceedings of the 5th Coastal Structures International Conference, CSt07. World Scientific Publishing Company, 2009. http://dx.doi.org/10.1142/9789814282024_0136.

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Utrera, Gladys, Marisa Gil, and Xavier Martorell. "Analyzing Data-Error Propagation Effects in High-Performance Computing." In 2016 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP). IEEE, 2016. http://dx.doi.org/10.1109/pdp.2016.42.

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Lin, Yao-San, and Tung-I. Tsai. "Using virtual data effects to stabilize pilot run neural network modeling." In 2013 IEEE International Conference on Grey Systems and Intelligent Services (GSIS). IEEE, 2013. http://dx.doi.org/10.1109/gsis.2013.6714828.

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Reports on the topic "Data Network Effects"

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Malde, Bansi, and Arun Advani. Empirical methods for networks data: social effects, network formation and measurement error. IFS, December 2014. http://dx.doi.org/10.1920/wp.ifs.2014.1434.

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Jochmans, Koen, and Martin Weidner. Fixed-effect regressions on network data. The IFS, May 2017. http://dx.doi.org/10.1920/wp.cem.2017.2617.

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Weidner, Martin, and Koen Jochmans. Fixed-effect regressions on network data. IFS, August 2016. http://dx.doi.org/10.1920/wp.cem.2016.3216.

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Weidner, Martin, and Koen Jochmans. Fixed-effect regressions on network data. The IFS, April 2019. http://dx.doi.org/10.1920/wp.cem.2019.1619.

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Latané, Annah, Jean-Michel Voisard, and Alice Olive Brower. Senegal Farmer Networks Respond to COVID-19. RTI Press, June 2021. http://dx.doi.org/10.3768/rtipress.2021.rr.0045.2106.

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This study leveraged existing data infrastructure and relationships from the Feed the Future Senegal Naatal Mbay (“flourishing agriculture”) project, funded by the US Agency for International Development (USAID) and implemented by RTI International from 2015 to 2019. The research informed and empowered farmer organizations to track and respond to rural households in 2020 as they faced the COVID-19 pandemic. Farmer organizations, with support from RTI and local ICT firm STATINFO, administered a survey to a sample of 800 agricultural households that are members of four former Naatal Mbay–supported farmer organizations in two rounds in August and October 2020. Focus group discussions were conducted with network leadership pre- and post–data collection to contextualize the experience of the COVID-19 shock and to validate findings. The results showed that farmers were already reacting to the effects of low rainfall during the 2019 growing season and that COVID-19 compounded the shock through disrupted communications and interregional travel bans, creating food shortages and pressure to divert seed stocks for food. Food insecurity effects, measured through the Household Food Insecurity Access Scale and cereals stocks, were found to be greater for households in the Casamance region than in the Kaolack and Kaffrine regions. The findings also indicate that farmer networks deployed a coordinated response comprising food aid and access to personal protective equipment, distribution of short-cycle legumes and grains (e.g., cowpea, maize) and vegetable seeds, protection measures for cereals seeds, and financial innovations with banks. However, food stocks were expected to recover as harvesting began in October 2020, and the networks were planning to accelerate seed multiplication, diversify crops beyond cereals, improve communication across the network. and mainstream access to financial instruments in the 2021 growing season. The research indicated that the previous USAID-funded project had likely contributed to the networks’ COVID-19 resilience capacities by building social capital and fostering the new use of tools and technologies over the years it operated.
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Dodd, Hope, David Peitz, Gareth Rowell, Janice Hinsey, David Bowles, Lloyd Morrison, Michael DeBacker, Jennifer Haack-Gaynor, and Jefrey Williams. Protocol for Monitoring Fish Communities in Small Streams in the Heartland Inventory and Monitoring Network. National Park Service, April 2021. http://dx.doi.org/10.36967/nrr-2284726.

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Fish communities are an important component of aquatic systems and are good bioindicators of ecosystem health. Land use changes in the Midwest have caused sedimentation, erosion, and nutrient loading that degrades and fragments habitat and impairs water quality. Because most small wadeable streams in the Heartland Inventory and Monitoring Network (HTLN) have a relatively small area of their watersheds located within park boundaries, these streams are at risk of degradation due to adjacent land use practices and other anthropogenic disturbances. Shifts in the physical and chemical properties of aquatic systems have a dramatic effect on the biotic community. The federally endangered Topeka shiner (Notropis topeka) and other native fishes have declined in population size due to habitat degradation and fragmentation in Midwest streams. By protecting portions of streams on publicly owned lands, national parks may offer refuges for threatened or endangered species and species of conservation concern, as well as other native species. This protocol describes the background, history, justification, methodology, data analysis and data management for long-term fish community monitoring of wadeable streams within nine HTLN parks: Effigy Mounds National Monument (EFMO), George Washington Carver National Monument (GWCA), Herbert Hoover National Historic Site (HEHO), Homestead National Monument of America (HOME), Hot Springs National Park (HOSP), Pea Ridge National Military Park (PERI), Pipestone National Monument (PIPE), Tallgrass Prairie National Preserve (TAPR), and Wilson's Creek national Battlefield (WICR). The objectives of this protocol are to determine the status and long-term trends in fish richness, diversity, abundance, and community composition in small wadeable streams within these nine parks and correlate the long-term community data to overall water quality and habitat condition (DeBacker et al. 2005).
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Bedoya-Maya, Felipe, Lynn Scholl, Orlando Sabogal-Cardona, and Daniel Oviedo. Who uses Transport Network Companies?: Characterization of Demand and its Relationship with Public Transit in Medellín. Inter-American Development Bank, September 2021. http://dx.doi.org/10.18235/0003621.

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Transport Network Companies (TNCs) have become a popular alternative for mobility due to their ability to provide on-demand flexible mobility services. By offering smartphone-based, ride-hailing services capable of satisfying specific travel needs, these modes have transformed urban mobility worldwide. However, to-date, few studies have examined the impacts in the Latin American context. This analysis is a critical first step in developing policies to promote efficient and sustainable transport systems in the Latin-American region. This research examines the factors affecting the adoption of on-demand ride services in Medellín, Colombia. It also explores whether these are substituting or competing with public transit. First, it provides a descriptive analysis in which we relate the usage of platform-based services with neighborhood characteristics, socioeconomic information of individuals and families, and trip-level details. Next, factors contributing to the election of platform-based services modeled using discrete choice models. The results show that wealthy and highly educated families with low vehicle availability are more likely to use TNCs compared to other groups in Medellín. Evidence also points at gender effects, with being female significantly increasing the probability of using a TNC service. Finally, we observe both transit complementary and substitution patterns of use, depending on the context and by whom the service is requested.
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Behrman, Jere R., Hans-Peter Kohler, and Susan Cotts Watkins. How can we measure the causal effects of social networks using observational data? Evidence from the diffusion of family planning and AIDS worries in South Nyanza District, Kenya. Rostock: Max Planck Institute for Demographic Research, July 2001. http://dx.doi.org/10.4054/mpidr-wp-2001-022.

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Balali, Vahid, Arash Tavakoli, and Arsalan Heydarian. A Multimodal Approach for Monitoring Driving Behavior and Emotions. Mineta Transportation Institute, July 2020. http://dx.doi.org/10.31979/mti.2020.1928.

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Studies have indicated that emotions can significantly be influenced by environmental factors; these factors can also significantly influence drivers’ emotional state and, accordingly, their driving behavior. Furthermore, as the demand for autonomous vehicles is expected to significantly increase within the next decade, a proper understanding of drivers’/passengers’ emotions, behavior, and preferences will be needed in order to create an acceptable level of trust with humans. This paper proposes a novel semi-automated approach for understanding the effect of environmental factors on drivers’ emotions and behavioral changes through a naturalistic driving study. This setup includes a frontal road and facial camera, a smart watch for tracking physiological measurements, and a Controller Area Network (CAN) serial data logger. The results suggest that the driver’s affect is highly influenced by the type of road and the weather conditions, which have the potential to change driving behaviors. For instance, when the research defines emotional metrics as valence and engagement, results reveal there exist significant differences between human emotion in different weather conditions and road types. Participants’ engagement was higher in rainy and clear weather compared to cloudy weather. More-over, engagement was higher on city streets and highways compared to one-lane roads and two-lane highways.
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Tucker-Blackmon, Angelicque. Engagement in Engineering Pathways “E-PATH” An Initiative to Retain Non-Traditional Students in Engineering Year Three Summative External Evaluation Report. Innovative Learning Center, LLC, July 2020. http://dx.doi.org/10.52012/tyob9090.

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The summative external evaluation report described the program's impact on faculty and students participating in recitation sessions and active teaching professional development sessions over two years. Student persistence and retention in engineering courses continue to be a challenge in undergraduate education, especially for students underrepresented in engineering disciplines. The program's goal was to use peer-facilitated instruction in core engineering courses known to have high attrition rates to retain underrepresented students, especially women, in engineering to diversify and broaden engineering participation. Knowledge generated around using peer-facilitated instruction at two-year colleges can improve underrepresented students' success and participation in engineering across a broad range of institutions. Students in the program participated in peer-facilitated recitation sessions linked to fundamental engineering courses, such as engineering analysis, statics, and dynamics. These courses have the highest failure rate among women and underrepresented minority students. As a mixed-methods evaluation study, student engagement was measured as students' comfort with asking questions, collaboration with peers, and applying mathematics concepts. SPSS was used to analyze pre-and post-surveys for statistical significance. Qualitative data were collected through classroom observations and focus group sessions with recitation leaders. Semi-structured interviews were conducted with faculty members and students to understand their experiences in the program. Findings revealed that women students had marginalization and intimidation perceptions primarily from courses with significantly more men than women. However, they shared numerous strategies that could support them towards success through the engineering pathway. Women and underrepresented students perceived that they did not have a network of peers and faculty as role models to identify within engineering disciplines. The recitation sessions had a positive social impact on Hispanic women. As opportunities to collaborate increased, Hispanic womens' social engagement was expected to increase. This social engagement level has already been predicted to increase women students' persistence and retention in engineering and result in them not leaving the engineering pathway. An analysis of quantitative survey data from students in the three engineering courses revealed a significant effect of race and ethnicity for comfort in asking questions in class, collaborating with peers outside the classroom, and applying mathematical concepts. Further examination of this effect for comfort with asking questions in class revealed that comfort asking questions was driven by one or two extreme post-test scores of Asian students. A follow-up ANOVA for this item revealed that Asian women reported feeling excluded in the classroom. However, it was difficult to determine whether these differences are stable given the small sample size for students identifying as Asian. Furthermore, gender differences were significant for comfort in communicating with professors and peers. Overall, women reported less comfort communicating with their professors than men. Results from student metrics will inform faculty professional development efforts to increase faculty support and maximize student engagement, persistence, and retention in engineering courses at community colleges. Summative results from this project could inform the national STEM community about recitation support to further improve undergraduate engineering learning and educational research.
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