Types Of Network Data Research Articles

Security Threat Early Warning of Distance Education System Based on Blockchain

<p>To ensure the safe and stable operation of distance education systems, a security threat early warning technology based on blockchain is proposed for distance education system, which builds a security threat warning model. It uses the data interface in the interface layer to connect the teacher and student client. Then, the network behavior data of the distance education system is collected and transmitted to the data layer, where data blocks exchange the behavior data of the distance education system, and then the chain structured behavior data is generated and transmitted to the consensus layer. After the behavior data is transmitted to the incentive layer through the consensus layer, the distribution mechanism and basis are used to process and transfer the behavior data to the contract layer. The contract layer uses the threat early warning model to calculate the behavior data, and then conducts threat rating and early warning response on the data. It transmits the threat rating and early warning results to the application layer and presents them to users, thus realizing the security threat early warning of the distance education system. The experimental results show that the transcoding rate of this technology for the network behavior data of the distance education system is higher than 0.97, the early warning accuracy for the 10 types of network data of the distance education system can reach 100%, and the credibility of the early warning security threat of the types of DDOS IP, DDOS IP, phishing website URL address, and mobile malicious server IP address is higher than 0.96. Therefore, the technology has a strong capacity of behavior data storage in distance education systems, and can effectively warn different types of security threat in distance education systems. It has a more excellent application effect.</p> <p> </p>

A method for analyzing text using VOSviewer

The need for technical support for data handling and visualization solutions has increased in tandem with the complexity of today's data and information, that is of multiple sources, huge in size and of different formats. This study focuses on handling and analyzing text-based data. Despite many available text analysis tools, there is a high demand among researchers for easy- to-use tools yet scalable and with incomparable visualization features. Of recent, there has been a significant focus on utilizing VOSviewer, an open-source software for bibliometric analysis. This software is able to analyze a significant amount of data and provide excellent network data mapping. However, there is a lack of existing work in evaluating this sophisticated tool for text analysis. Thus, this article explores the capability of VOSviewer and presents evidence-based implementation of this software for text analysis.Specifically, this study demonstrates the usage of VOSviewer to analyze text based on YouTube interviews related to ChatGPT. Hence, this study significantly contributes by processing textual data and producing visualization network maps that are different from bibliometric data. The study recognizes VOSviewer as a powerful tool for data visualization in mapping text data and illustrates the potential of this software for analyzing text networks in various fields.•The study illustrates how text analysis and visualization can be realized using VOSviewer, an open-source software mostly used for biblio- metric analysis.•The study presents the workflow indicating how the dataset can be prepared as input for VOSviewer for text analysis.•The study proves that VOSviewer is a powerful tool for data visualization and network mapping for any type of network data including transcripts from social media.

IdopNetwork: A network tool to dissect spatial community ecology

Abstract Network models have been used as a tool to characterize internal workings of complex systems. The amount of topological and functional information extracted from a network depend on the method of network inference and the type of network data. An interdisciplinary computational model has been proposed to reconstruct informative, dynamic, omnidirectional and personalized networks (idopNetwork) from any data domains including static data. We implement idopNetwork as an R‐based cartographic tool to characterize spatially varying interspecies interaction networks using the abundance data of multiple species from different geographical locations. This tool provides a unified framework for integrating power curve fitting based on allometrical scaling law, functional clustering, LASSO‐based variable selection, quasi‐dynamic ordinary differential equation solving, species abundance decomposition and network visualization. It coalesces all species from different spaces into location‐specific networks. We demonstrate the utility of this tool by analysing different organs that are spatially interconnected via microbiomes within the host using two datasets from the gut microbiota and plant microbiota. Given that biodiversity and organization vary biogeographically at different scales, idopNetwork will find its widespread application to modelling and estimating interspecific interactions with differing functions across space.

Security Detection Design for Laboratory Networks Based on Enhanced LSTM and AdamW Algorithms

With the addition of multimedia big data, the diversification of network data types becomes more prominent, the proportion of unstructured data increases sharply, and the requirements of data application show the characteristics of rapid change. Laboratory servers hold a large amount of core experimental data, which is at risk of being compromised in the event of a cyberattack, and the rapid pace of information technology has made cyberattacks complex. In the face of the great challenges posed by continuously changing networks to network security, this paper proposes a network exception detection approach that combines an improved inception module incorporating an attention mechanism and a Bi-LSTM. The inception module with the attention mechanism enhances the adaptability of the neural network to different spatial feature scales in the network stream, weakens irrelevant non-critical features, and exploits the advantages of the Bi-LSTM in terms of temporal features of the network stream to effectively improve the accuracy of the detection of network attacks.

Ergm 4: New Features for Analyzing Exponential-Family Random Graph Models

The ergm package supports the statistical analysis and simulation of network data. It anchors the statnet suite of packages for network analysis in R introduced in a special issue in Journal of Statistical Software in 2008. This article provides an overview of the new functionality in the 2021 release of ergm version 4. These include more flexible handling of nodal covariates, term operators that extend and simplify model specification, new models for networks with valued edges, improved handling of constraints on the sample space of networks, and estimation with missing edge data. We also identify the new packages in the statnet suite that extend ergm's functionality to other network data types and structural features and the robust set of online resources that support the statnet development process and applications.

Conducting Physical Activity Research on Racially and Ethnically Diverse Adolescents Using Social Network Analysis: Case Studies for Practical Use.

Adolescent physical activity (PA) is significantly impacted by peer behaviors through peer influence, peer selection, and popularity. However, the scales for these social constructs may not fully capture the detailed social networks and mechanisms responsible for PA behavior changes. This level of detail and granularity can be quantified and analyzed through social network analysis (SNA). To demonstrate the variety, utility, and efficacy of SNA in adolescent PA research, this article aims to provide four case studies on the collection of social network and PA data on ethnically and racially diverse adolescents. Through case studies, this article provides tangible ways in which SNA can be used to evaluate social influences on PA behaviors. Case studies are presented on: (1) Youth Engagement in Sport—an egocentric analysis of middle school youth participation in an experiential sport program with 3- and 6-month follow-ups; (2) Summer care program networks—an egocentric and whole network longitudinal study of adolescents at summer care programs; (3) The Convoy method—a qualitative egocentric discussion activity with adolescents from colonias on the Texas-Mexico border; and (4) A father-focused, family-centered health program—an egocentric experimental analysis of children participating in a health program. Data collection procedures are listed and example surveys are provided. Descriptive analyses are included, as are recommendations on further analysis techniques for each type of network data. Using SNA, researchers can understand social contexts in a more specific manner, better positioning interventions to alter such influences.

Hypothesis Testing for Network Data with Power Enhancement.

Comparing two population means of network data is of paramount importance in a wide range of scientific applications. Numerous existing network inference solutions focus on global testing of entire networks, without comparing individual network links. The observed data often take the form of vectors or matrices, and the problem is formulated as comparing two covariance or precision matrices under a normal or matrix normal distribution. Moreover, many tests suffer from a limited power under a small sample size. In this article, we tackle the problem of network comparison, both global and simultaneous inferences, when the data come in a different format, i.e., in the form of a collection of symmetric matrices, each of which encodes the network structure of an individual subject. Such data format commonly arises in applications such as brain connectivity analysis and clinical genomics. We no longer require the underlying data to follow a normal distribution, but instead impose some moment conditions that are easily satisfied for numerous types of network data. Furthermore, we propose a power enhancement procedure, and show that it can control the false discovery, while it has the potential to substantially enhance the power of the test. We investigate the efficacy of our testing procedure through both an asymptotic analysis and a simulation study under a finite sample size. We further illustrate our method with examples of brain connectivity analysis.

Impact of survey design on estimation of exponential-family random graph models from egocentrically-sampled data

Egocentric sampling of networks selects a subset of nodes (“egos”) and collects information from them on themselves and their immediate network neighbours (“alters”), leaving the rest of the nodes in the network unobserved. This design is popular because it is relatively inexpensive to implement and can be integrated into standard sample surveys. Recent methodological developments now make it possible to statistically analyse this type of network data with exponential-family random graph models (ERGMs). This provides a framework for principled statistical inference, and the fitted models can in turn be used to simulate complete networks of arbitrary size that are consistent with the observed sample data, allowing one to infer the distribution of whole-network properties generated by the observed egocentric network statistics. In this paper, we discuss how design choices for egocentric network studies impact statistical estimation and inference for ERGMs. The design choices include both measurement strategies (for ego and alter attributes, and for ego–alter and alter–alter ties) and sampling strategies (for egos and alters). We discuss the importance of harmonising measurement specifications across egos and alters, and conduct simulation studies to demonstrate the impact of sampling design on statistical inference, specifically stratified sampling and degree censoring.

Using network ecology to understand and mitigate long‐term insect declines

Using network ecology to understand and mitigate long‐term insect declines

Additive and Multiplicative Effects Network Models

Network datasets typically exhibit certain types of statistical patterns, such as within-dyad correlation, degree heterogeneity, and triadic patterns such as transitivity and clustering. The first two of these can be well represented with a social relations model, a type of additive effects model originally developed for continuous dyadic data. Higher-order patterns can be represented with multiplicative effects models, which are related to matrix decompositions that are commonly used for matrix-variate data analysis. Additionally, these multiplicative effects models generalize other popular latent feature network models, such as the stochastic blockmodel and the latent space model. In this article, we review a general regression framework for the analysis of network data that combines these two types of effects, and accommodates a variety of network data types, including continuous, binary and ordinal network relations.

The performance of permutations and exponential random graph models when analyzing animal networks

Abstract Social network analysis is a suite of approaches for exploring relational data. Two approaches commonly used to analyze animal social network data are permutation-based tests of significance and exponential random graph models. However, the performance of these approaches when analyzing different types of network data has not been simultaneously evaluated. Here we test both approaches to determine their performance when analyzing a range of biologically realistic simulated animal social networks. We examined the false positive and false negative error rate of an effect of a two-level explanatory variable (e.g., sex) on the number and combined strength of an individual’s network connections. We measured error rates for two types of simulated data collection methods in a range of network structures, and with/without a confounding effect and missing observations. Both methods performed consistently well in networks of dyadic interactions, and worse on networks constructed using observations of individuals in groups. Exponential random graph models had a marginally lower rate of false positives than permutations in most cases. Phenotypic assortativity had a large influence on the false positive rate, and a smaller effect on the false negative rate for both methods in all network types. Aspects of within- and between-group network structure influenced error rates, but not to the same extent. In "grouping event-based" networks, increased sampling effort marginally decreased rates of false negatives, but increased rates of false positives for both analysis methods. These results provide guidelines for biologists analyzing and interpreting their own network data using these methods.

Learning Vertex Representations for Bipartite Networks

Recent years have witnessed a widespread increase of interest in network representation learning (NRL). By far most research efforts have focused on NRL for homogeneous networks like social networks where vertices are of the same type, or heterogeneous networks like knowledge graphs where vertices (and/or edges) are of different types. There has been relatively little research dedicated to NRL for bipartite networks. Arguably, generic network embedding methods like node2vec and LINE can also be applied to learn vertex embeddings for bipartite networks by ignoring the vertex type information. However, these methods are suboptimal in doing so, since real-world bipartite networks concern the relationship between two types of entities, which usually exhibit different properties and patterns from other types of network data. For example, E-Commerce recommender systems need to capture the collaborative filtering patterns between customers and products, and search engines need to consider the matching signals between queries and webpages. This work addresses the research gap of learning vertex representations for bipartite networks. We present a new solution BiNE, short for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Bi</b> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">partite</i> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</b> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">etwork</i> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</b> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mbedding</i> , which accounts for two special properties of bipartite networks: long-tail distribution of vertex degrees and implicit connectivity relations between vertices of the same type. Technically speaking, we make three contributions: (1) We design a biased random walk generator to generate vertex sequences that preserve the long-tail distribution of vertices; (2) We propose a new optimization framework by simultaneously modeling the explicit relations (i.e., observed links) and implicit relations (i.e., unobserved but transitive links); (3) We explore the theoretical foundations of BiNE to shed light on how it works, proving that BiNE can be interpreted as factorizing multiple matrices. We perform extensive experiments on five real datasets covering the tasks of link prediction (classification) and recommendation (ranking), empirically verifying the effectiveness and rationality of BiNE. Our experiment codes are available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/clhchtcjj/BiNE</uri> .

Implementasi Metode Model View Controller (MVC) Dalam Rancang Bangun Website SMK Yayasan Bakti Prabumulih

Website or site may be defined as a collection of pages displaying text data, still or motion picture data, animation data, sound, video and or all of them, both static and dynamic, forming a series of interconnected buildings in which each linked to the network pages (hyperlinks). The school website has a very important role in education. SMK Bakti Prabumulih Foundation is one of the private schools that have two majors, automotive motorcycle and computer engineering and networking techniques, but this school does not have a website so many people who do not know yet remember this school is still new. In making this website the author also apply the MVC method as an option in the development of the system. The View Controller (MVC) model is a concept introduced by the inventor of Smalltalk (Trygve Reenskaug) to create one type of network data into another data type along with processing (model), isolating from the process of manipulation (controller) and view to be represented on a user interface. This application is made using the programming language Personal Hypertext Preprocessor (PHP) as a programming language and design editor Macromedia Dreamweaver, and MySQL as its database so it can help penginputan data quickly, precisely and efficiently.

A comprehensive survey on network anomaly detection

Nowadays, there is a huge and growing concern about security in information and communication technology among the scientific community because any attack or anomaly in the network can greatly affect many domains such as national security, private data storage, social welfare, economic issues, and so on. Therefore, the anomaly detection domain is a broad research area, and many different techniques and approaches for this purpose have emerged through the years. In this study, the main objective is to review the most important aspects pertaining to anomaly detection, covering an overview of a background analysis as well as a core study on the most relevant techniques, methods, and systems within the area. Therefore, in order to ease the understanding of this survey’s structure, the anomaly detection domain was reviewed under five dimensions: (1) network traffic anomalies, (2) network data types, (3) intrusion detection systems categories, (4) detection methods and systems, and (5) open issues. The paper concludes with an open issues summary discussing presently unsolved problems, and final remarks.

Social Network Modeling

The development of stochastic models for the analysis of social networks is an important growth area in contemporary statistics. The last few decades have witnessed the rapid development of a variety of statistical models capable of representing the global structure of an observed network in terms of underlying generating mechanisms. The distinctive feature of statistical models for social networks is their ability to represent directly the dependence relations that these mechanisms entail. In this review, we focus on models for single network observations, particularly on the family of exponential random graph models. After defining the models, we discuss issues of model specification, estimation and assessment. We then review model extensions for the analysis of other types of network data, provide an empirical example, and give a selective overview of empirical studies that have adopted the basic model and its many variants. We conclude with an outline of the current analytical challenges.

Online social network data as sociometric markers.

Data from online social networks carry enormous potential for psychological research, yet their use and the ethical implications thereof are currently hotly debated. The present work aims to outline in detail the unique information richness of this data type and, in doing so, to support researchers when deciding on ethically appropriate ways of collecting, storing, publishing, and sharing data from online sources. Focusing on the very nature of social networks, their structural characteristics, and depth of information, we provide a detailed and accessible account of the challenges associated with data management and data storage. In particular, the general nonanonymity of network data sets is discussed, and an approach is developed to quantify the level of uniqueness that a particular online network bestows upon the individual maintaining it. Using graph enumeration techniques, we show that comparatively sparse information on a network is suitable as a sociometric marker that allows for the identification of an individual from the global population of online users. The impossibility of anonymizing specific types of network data carries implications for ethical guidelines and research practice. At the same time, network uniqueness opens up opportunities for novel research in psychology. (PsycINFO Database Record

The application of statistical network models in disease research

Summary Host social structure is fundamental to how infections spread and persist, and so the statistical modelling of static and dynamic social networks provides an invaluable tool to parameterise realistic epidemiological models. We present a practical guide to the application of network modelling frameworks for hypothesis testing related to social interactions and epidemiology, illustrating some approaches with worked examples using data from a population of wild European badgers Meles meles naturally infected with bovine tuberculosis. Different empirical network datasets generate particular statistical issues related to non‐independence and sampling constraints. We therefore discuss the strengths and weaknesses of modelling approaches for different types of network data and for answering different questions relating to disease transmission. We argue that statistical modelling frameworks designed specifically for network analysis offer great potential in directly relating network structure to infection. They have the potential to be powerful tools in analysing empirical contact data used in epidemiological studies, but remain untested for use in networks of spatio‐temporal associations. As a result, we argue that developments in the statistical analysis of empirical contact data are critical given the ready availability of dynamic network data from bio‐logging studies. Furthermore, we encourage improved integration of statistical network approaches into epidemiological research to facilitate the generation of novel modelling frameworks and help extend our understanding of disease transmission in natural populations.

GTS Scheduling Scheme for Real-Time Communication in IEEE802.15.4 Industrial Wireless Sensor Networks

Background/Objectives: We have proposed an improved algorithm for the delay sensitive networks with bulk periodic data traffic. This kind of networks such as Industrial networks that should transfer the monitoring data has a high sensitivity to delay since these networks should work real-time. Methods/Statistical analysis: Considering and studying the industrial network traffic model, a new adaptive centralized GTS scheduling algorithm is proposed focusing on real-time communication and reliability which are two important issues in industrial wireless communication. Findings: Wireless Sensor Network (WSN) is selected for implementing the industrial network. Industrial Wireless Sensor Network (IWSN) and IEEE802.15.4 standard is the most popular standard for WSN MAC layer protocol. In this paper considering the industrial network data types new GTS scheduling scheme for IEEE802.15.4 is introduced for the delay sensitive networks with bulk periodic data traffic. Applications/Improvements: Our proposed algorithm by adapting the characteristics of industrial networks and IEEE802.15.4 standard shows high gain in delay improvement.

Sampling dynamic networks with application to investigation of HIV epidemic drivers

We propose a method for randomly sampling dynamic networks that permits isolation of the impact of different network features on processes that propagate on networks. The new methods permit uniform sampling of dynamic networks in ways that ensure that they are consistent with both a given cumulative network and with specified values for constraints on the dynamic network properties. Development of such methods is challenging because modifying one network property will generally tend to modify others as well. Methods to sample constrained dynamic networks are particularly useful in the investigation of network-based interventions that target and modify specific dynamic network properties, especially in settings where the whole network is unobservable and therefore many network properties are unmeasurable. We illustrate this method by investigating the incremental impact of changes in networks properties that are relevant for the spread of infectious diseases, such as concurrency in sexual relationships. Development of the method is motivated by the challenges that arise in investigating the role of HIV epidemic drivers due to the often limited information available about contact networks. The proposed methods for randomly sampling dynamic networks facilitate investigation of the type of network data that can best contribute to an understanding of the HIV epidemic dynamics as well as of the limitations of conclusions drawn in the absence of such information. Hence, the methods are intended to aid in the design and interpretation of studies of network-based interventions.

Taxonomy of social network data types

Online social networks (OSNs) have become an integral part of social interaction and communication between people. Reasons include the ubiquity of OSNs that is offered through mobile devices and the possibility to bridge spatial and temporal communication boundaries. However, several researchers have raised privacy concerns due to the large amount of user data shared on OSNs. Yet, despite the large body of research addressing OSN privacy issues, little differentiation of data types on social network sites is made and a generally accepted classification and terminology for such data is missing. The lack of a terminology impedes comparability of related work and discussions among researchers, especially in the case of privacy implications of different data types. To overcome these shortcomings, this paper develops a well-founded terminology based on a thorough literature analysis and a conceptualization of typical OSN user activities. The terminology is organized hierarchically resulting in a taxonomy of data types. The paper furthermore discusses and develops a metric to assess the privacy relevance of different data types. Finally, the taxonomy is applied to the five major OSNs to evaluate its generalizability.