Network Analysis Of Data Research Articles

Using an auxiliary dataset to improve emotion estimation in users’ opinions

Sentimental analysis of social networking data is an economically affordable and effective way to track and evaluate public viewpoints that are critical for decision making in different areas. Predicting the users’ future opinions is crucial for companies and services; if companies understand users’ sentiments in considered time frames, they can do much better by knowing where exactly users are satisfied or unsatisfied. Utilizing an auxiliary dataset, this study uses the opinions of users on the Twitter social network expressed in the form of short text, and presents the Auxiliary Dataset-Latent Dirichlet Allocation (AD-LDA) model to improve the learning of users’ emotions around a specific topic. The proposed model considers the emotions –as predefined sentiments with a wide sentimental outlook– to estimate users’ feelings and sentiments about a particular subject or event. Coherence score evaluation results for the four studied hashtags showed an average 64.15% improvement compared to the conventional LDA model. The average Weighted-F1 criteria for studied hashtags was 79.83% for the accuracy of learning. Experimental and evaluation results show that our proposed model can effectively learn the emotions of words which leads to a better understanding of users’ feelings.

Testing for association in multiview network data

In this paper, we consider data consisting of multiple networks, each composed of a different edge set on a common set of nodes. Many models have been proposed for the analysis of such multiview network data under the assumption that the data views are closely related. In this paper, we provide tools for evaluating this assumption. In particular, we ask: given two networks that each follow a stochastic block model, is there an association between the latent community memberships of the nodes in the two networks? To answer this question, we extend the stochastic block model for a single network view to the two-view setting, and develop a new hypothesis test for the null hypothesis that the latent community memberships in the two data views are independent. We apply our test to protein-protein interaction data from the HINT database. We find evidence of a weak association between the latent community memberships of proteins defined with respect to binary interaction data and the latent community memberships of proteins defined with respect to cocomplex association data. We also extend this proposal to the setting of a network with node covariates. The proposed methods extend readily to three or more network/multivariate dataviews.

Network representation using graph root distributions

Exchangeable random graphs serve as an important probabilistic framework for the statistical analysis of network data. In this work, we develop an alternative parameterization for a large class of exchangeable random graphs, where the nodes are independent random vectors in a linear space equipped with an indefinite inner product, and the edge probability between two nodes equals the inner product of the corresponding node vectors. Therefore, the distribution of exchangeable random graphs in this subclass can be represented by a node sampling distribution on this linear space, which we call the graph root distribution. We study existence and identifiability of such representations, the topological relationship between the graph root distribution and the exchangeable random graph sampling distribution and estimation of graph root distributions.

Скулшутинг в России: что имеет значение?

The paper presents the results of sociological study of school shooting in Russia using the case-study method; triangulation of data from different information sources; prototypes developed by interdisciplinary project team of TPU for intellectual analysis of social networks data. On the base of the category of school shooting formulated in a generalized form and the law enforcement practice existing in Russia, a research sample was formed, including 23 cases. All the cases are marked according to three types (based on foreign practice of the singles typology): school shooter, mass murderer, terrorist. 19 cases were definitely defined as school shooters. The authors identify types of imitation and develop a scheme of relationships; mark significant signs; carry out the analysis of dynamics of cases; present examples of warning signs which school shooters leave on the Internet; mark the basic and additional motives of school shooters; present results of studying social connections of school shooters in the online environment.

Factor graph-aggregated heterogeneous network embedding for disease-gene association prediction

BackgroundExploring the relationship between disease and gene is of great significance for understanding the pathogenesis of disease and developing corresponding therapeutic measures. The prediction of disease-gene association by computational methods accelerates the process.ResultsMany existing methods cannot fully utilize the multi-dimensional biological entity relationship to predict disease-gene association due to multi-source heterogeneous data. This paper proposes FactorHNE, a factor graph-aggregated heterogeneous network embedding method for disease-gene association prediction, which captures a variety of semantic relationships between the heterogeneous nodes by factorization. It produces different semantic factor graphs and effectively aggregates a variety of semantic relationships, by using end-to-end multi-perspectives loss function to optimize model. Then it produces good nodes embedding to prediction disease-gene association.ConclusionsExperimental verification and analysis show FactorHNE has better performance and scalability than the existing models. It also has good interpretability and can be extended to large-scale biomedical network data analysis.

THE WORLD CITATION NETWORK POSITION OF RUSSIAN PUBLICATIONS ON THE INFLUENCE OF THE STUDENTS’ INTERNET ACTIVITY ON THEIR EDUCATIONAL ACHIEVEMENTS

Over the last 20 years the educational environment has undergone a number of changes, one of the factors of these changes is the introduction of Internet opportunities in the educational process. Due to the expansion of the Internet, in particular, in the field of education, there is a surge of research interest in the world on the impact of the Internet on educational achievements. This work is devoted to the problem of the position of Russian publications in the global network of quotations on the theme of the relationship between Internet activity and educational achievements of students. In the course of the research, a rarely used method of reviewing sources on a given topic was proposed based on a combination of two approaches of literary review - expert and bibliometric network analysis. The results of the research demonstrate a global network of citations of the most popular publications on the subject of Internet activity and educational achievements, and also reflect the position of Russian authors' publications in relation to the whole network. It was found that: 1) the prevailing literature on the subject under study is foreign, foreign sources present the main theoretical and methodological foundations and applied research; 2) Russian scientists are at the beginning of mastering the analysis of social network data and studying the impact of the Internet on educational achievements, which explains the small number of publications.

Innovated scalable efficient inference for ultra-large graphical models

Statistical inference for ultra-large graphical models is important in network data analysis. We exploit the innovated scalable efficient estimation (Fan and Lv, 2016) as an initial estimate to develop a scalable inference procedure for graphical models. The effectiveness of the proposed method is theoretically and numerically demonstrated.

Supported, detached, or marginalized? The ambivalent role of social capital on stress at work

Relationships with work colleagues can mitigate job stress; this article proposes a new perspective on such effects, in accordance with social capital theory, to delineate the potentially distinct impacts of the two dimensions of relational and structural social capital. A partial least squares analysis of network data in a medium-sized company (N = 343) offers evidence of an effect of the relational dimension (tie strength), through social support. The structural dimension (bridging ties, i.e. whether an employee has social ties with members of other departments) reveals a dual effect involving reduced stress but also diminished social support, which can increase stress. These dual effects reflect the two levels on which bridging ties act, namely, cognitive (selfdistancing) and social (marginalization from the immediate work context) levels. Next, with two scenario-based experiments (N = 289 and 245), the authors manipulate bridging ties and gain further evidence of the proposed mechanisms. These findings highlight the social underpinnings of stress and the critical role of the structure, not just the quality, of work relationships. They also identify a dark side of bridging ties, which can enhance individual performance but also create the potential for negative wellbeing outcomes.

Linguistic Analysis of Internet Media and Social Network Data in the Problems of Social Transformation Assessment.

A combined approach has been developed to estimate the effectiveness of social transformations as a measure of inconsistency between the actions of the authorities and the expectations of society and the synergy (social activity) of people, based on formalized coordination of the results obtained by the method of expert assessments and methods of sentiment analysis and intelligent analysis of text messages from online open sources and social networks. These methods are implemented as a set of web services and applications in the development environment of the Advanced Analytics integrated online platform of the World Data Center “Geoinformatics and Sustainable Development.” The effectiveness of the proposed approach is demonstrated by a quantitative assessment of the attitude of the population of Ukraine to the actions of the authorities concerned with the spread of the COVID-19 pandemic.

Dimension reduction for covariates in network data

Summary A problem of major interest in network data analysis is to explain the strength of connections using context information. To achieve this, we introduce a novel approach, called network-supervised dimension reduction, in which covariates are projected onto low-dimensional spaces to reveal the linkage pattern without assuming a model. We propose a new loss function for estimating the parameters in the resulting linear projection, based on the notion that closer proximity in the low-dimension projection corresponds to stronger connections. Interestingly, the convergence rate of our estimator is found to depend on a network effect factor, which is the smallest number that can partition a graph in a manner similar to the graph colouring problem. Our method has interesting connections to principal component analysis and linear discriminant analysis, which we exploit for clustering and community detection. The proposed approach is further illustrated by numerical experiments and analysis of a pulsar candidates dataset from astronomy.

A practical two-sample test for weighted random graphs

Network (graph) data analysis is a popular research topic in statistics and machine learning. In application, one is frequently confronted with graph two-sample hypothesis testing where the goal is to test the difference between two graph populations. Several statistical tests have been devised for this purpose in the context of binary graphs. However, many of the practical networks are weighted and existing procedures cannot be directly applied to weighted graphs. In this paper, we study the weighted graph two-sample hypothesis testing problem and propose a practical test statistic. We prove that the proposed test statistic converges in distribution to the standard normal distribution under the null hypothesis and analyze its power theoretically. The simulation study shows that the proposed test has satisfactory performance and it substantially outperforms the existing counterpart in the binary graph case. A real data application is provided to illustrate the method.

Feature Extraction of Ancient Chinese Characters Based on Deep Convolution Neural Network and Big Data Analysis.

In recent years, deep learning has made good progress and has been applied to face recognition, video monitoring, image processing, and other fields. In this big data background, deep convolution neural network has also received more and more attention. In order to extract the ancient Chinese characters effectively, the paper will discuss the structure model, pool process, and network training of deep convolution neural network and compare the algorithm with the traditional machine learning algorithm. The results show that the accuracy and recall rate of the Chinese characters in the plaque of Ming Dynasty can reach the peak, 81.38% and 81.31%, respectively. When the number of training samples increases to 50, the recognition rate of MFA is 99.72%, which is much higher than other algorithms. This shows that the algorithm based on deep convolution neural network and big data analysis has excellent performance and can effectively identify the Chinese characters under different dynasties, different sample sizes, and different interference factors, which can provide a powerful reference for the extraction of ancient Chinese characters.

5G network slice for digital real-time healthcare system powered by network data analytics

In the wake of the COVID-19 pandemic, where almost the entire global healthcare ecosystem struggled to handle patients, it’s evident that the healthcare segment needs a virtual real-time digital support system. The recent advancements in technology have enabled machine-to-machine communication, enhanced mobile broadband, and real-time biometric data analytics. These could potentially fulfill the requirements of an end-to-end digital healthcare system. For building such a system, there is also a need for a dedicated and specialized communication network. Such a system will not only support dynamic throughput, latency and payload but also provide guaranteed QoS (Quality of Service) at every instant. The motive of our study was to define an implementable low-level architecture for the digital healthcare system by using the 5G Network Slice that incorporates all these features. Best-in-class wearable devices will collect the biometric data and transmit it via the 5G network slice. Data analytics is then applied to the collected data to build a knowledge graph used for quick predictions and prescriptions. The architecture also keeps in mind the security and integrity aspects of healthcare data.

Social Network Big Data Hierarchical High‐Quality Node Mining

Compared with the conventional network data analysis, the data analysis based on social network has a very clear object of analysis, various forms of analysis, and more methods and contents of analysis. If the conventional analysis methods are applied to social network data analysis, we will find that the analysis results do not reach our expected results. The results of the above studies are usually based on statistical methods and machine learning methods, but some systems use other methods, such as self‐organizing self‐learning mechanisms and concept retrieval. With regard to the current data analysis methods, data models, and social network data, this paper conducts a series of researches from data acquisition, data cleaning and processing, data model application and optimization of the model in the process of application, and how the formed data analysis results can be used for managers to make decisions. In this paper, the number of customer evaluations, the time of evaluation, the frequency of evaluation, and the score of evaluation are clustered and analyzed, and finally, the results obtained by the two clustering methods applied in the analysis process are compared to build a customer grading system. The analysis results can be used to maintain the current Amazon purchase customers in a hierarchical manner, and the most valuable customers need to be given key attention, combining social network big data with micro marketing to improve Amazon’s sales performance and influence, developing from the original single shopping mall model to a comprehensive e‐commerce platform, and cultivating their own customer base.

Crawling subsampling for multivariate spatial autoregression model in large-scale networks

In network data analysis, multivariate spatial autoregression (MSAR) models may be used to analyze the autocorrelation among multiple responses. With large-scale networks, the estimation for MSAR on the entire network is computationally expensive. In this case, the subsampling method could be adopted. This approach selects a sample of nodes and then uses the estimate based on the sample to approximate the estimate on the full data. However, traditional sampling methods cannot obtain unbiased parameter estimates. Considering the second-order friend information of sampled nodes, we propose the crawling subsampling (CS) method for a general framework. Thereafter, based on the sampled data only, we construct the least-squares objective function. Under certain conditions, the computational complexity of optimizing the objective function is linear with the sample size ns. The identification condition for the parameters on the sampled network is theoretically provided. The sample size order requirement is provided, and the asymptotic properties of the least-squares estimators are investigated. The numerical results for the simulated and real data are presented to demonstrate the performance of the proposed CS method and least-squares estimator for the MSAR model.

Survey of Tools and Techniques for Sentiment Analysis of Social Networking Data

Social media has rapidly expanded over a period of time and generated a huge repository of content. Sentiment analysis of this data has a vast scope in decision support and attracted many researchers to explore various possibilities for technique enhancement and accuracy improvement. Twitter is one of the social media platforms that are widely explored in the area of sentiment analysis. This paper presents a systematic survey related to Social Networking Sites Sentiment Analysis and mainly focus on Twitter sentiment analysis. The paper explores and identifies the techniques and tools used in a well-structured approach to find out the research gaps and identify future scope in this area of research. The techniques evolved over time to improve the efficiency of classification. Total 55 research papers are included in this survey. The result reflects that Twitter is the most explored social networking site for opinion mining. Naïve Bayes and SVM machine learning algorithms are implemented in maximum researches. As the latest advancements, Stack based ensemble, fuzzy based and neural network based classifiers are also implemented to enhance the efficiency of classification. WEKA, R Studio, Python are mostly used tools by research scholars for implementation. The overall evolution of the research goes through various changes in terms of technologies, tools, social media platforms and data corpus targeted.

Case Study on Intelligent Classroom Teaching Based on Internet + Education

With the development of technology, intelligent classroom emerges as the times require. It is the result of the combination of emerging technology and traditional teaching, and has become the forefront of current education research content. With the help of network and big data analysis, this paper applies the smart classroom to English teaching, achieves the comprehensive analysis of English curriculum, expands students' learning time and space, greatly stimulates students' learning enthusiasm, changes the boring teaching in traditional classroom, and creates an intelligent learning atmosphere.

Feature Recognition of English Based on Deep Belief Neural Network and Big Data Analysis.

Realizing accurate recognition of Chinese and English information is a major difficulty in English feature recognition. Based on this difficulty, this paper studies the English feature recognition model based on deep belief network classification algorithm and Big Data analysis. First, the basic framework based on deep belief network classification algorithm and Big Data analysis is proposed. Combined with the Big Data analysis training model, the English feature information is processed. Through the recognition of different English text features, the recognition and matching of English features are realized. Then the errors of deep belief network classification algorithm and Big Data analysis are evaluated. Second, this paper describes the quantitative evaluation of deep belief network classification algorithm and Big Data analysis in this system. In the evaluation, the language feature evaluation method is used to improve the evaluation function. At the same time, the deep belief network classification algorithm and Big Data analysis are used to self-study the model, and the English feature recognition method with strong applicability is established. Finally, the effectiveness of the recognition system is verified by the experiment.

Music Emotion Analysis Based on PSO-BP Neural Network and Big Data Analysis.

The current music teaching can effectively improve students' music emotional expression indirectly. How to use the PSO-BP neural network to realize the quantitative research of music emotional expression is the current development trend. Based on this, this paper studies the influence factors of music emotion expression based on PSO-BP neural network and big data analysis. Firstly, a music emotion expression analysis model based on PSO-BP neural network algorithm is proposed. The autocorrelation function is used to simulate the emotion expression information in music. Through the maximum value of the autocorrelation function curve in the detection process, the vocal music signal is restored, and then the emotion expressed is analyzed. Secondly, the influence factors of PSO-BP neural network algorithm in music emotion expression are analyzed. The improved PSO-BP neural network algorithm and multidimensional data model are used for comprehensive analysis to accurately analyze the emotion in music expression, and the fuzzy evaluation method and analytic hierarchy process are used for quality evaluation. Finally, the validity of the music emotion analysis model is verified by many experiments.

Sports Action Recognition Based on GB-BP Neural Network and Big Data Analysis.

In recent years, the application of the gradient boosting-back propagation (GB-BP) neural network algorithm in many industries has brought huge benefits, so how to combine the GB-BP neural network algorithm with sports has become a research hotspot. Based on this, this paper studies the application of the GB-BP neural network algorithm in wrestling, designs the sports athletes action recognition and classification model based on the GB-BP neural network algorithm, first analyzes the research status of wrestling action recognition, and then optimizes and improves the shortcomings of action recognition and big data analysis technology. The GB-BP neural network algorithm can realize the accurate recognition and classification of wrestlers' training actions and carry out big data mining analysis with known action recognition, so as to achieve accurate classification. The experimental results show that the model can play a good role in wrestling and effectively improve the efficiency of wrestlers in training.