Network Analysis Of Data Research Articles

Postal and trade network data within ASEAN countries and beyond

This study mainly examines the statistical analysis of Postal Network Data (PND) and Trade Data within ASEAN countries and beyond. In addition, based on the previous study on the global network structure, including postal network, as proxies for national well-being, we also assess how the PND can affect the other recent socioeconomic indicators among ASEAN countries. This study aims to address the general question of whether structural network properties of different flow networks between ASEAN countries can be used to produce proxy indicators for the socioeconomic profile of a country. Moreover, we are using statistical analysis just like the correlation to measure the variables post from and post to data with life expectancy, CPI, mobile subscriber, Internet penetration, fixed phone, HDI, GDP and CO2 emission. After getting the correlation value. The next step we do partial least square (PLS) on the model we have built before. Just as getting 3 cluster component based on the data. Also, Matrix of the intensity connection is used to understand also compare the positions of countries within the different networks several socioeconomic indicators

Graph Signal Processing: Overview, Challenges, and Applications

Research in graph signal processing (GSP) aims to develop tools for processing data defined on irregular graph domains. In this paper, we first provide an overview of core ideas in GSP and their connection to conventional digital signal processing, along with a brief historical perspective to highlight how concepts recently developed in GSP build on top of prior research in other areas. We then summarize recent advances in developing basic GSP tools, including methods for sampling, filtering, or graph learning. Next, we review progress in several application areas using GSP, including processing and analysis of sensor network data, biological data, and applications to image processing and machine learning.

An Integrative Framework for Protein Interaction Network and Methylation Data to Discover Epigenetic Modules.

DNA methylation is a critical epigenetic modification that plays an important role in cancers. The available algorithms fail to fully characterize epigenetic modules. To address this issue, we first characterize the epigenetic module as a group of well-connected genes in the protein interaction network and are also co-methylated based on gene methylation profiles. Then, the epigenetic module discovery problem is transformed into an optimization problem. Then, a regularized nonnegative matrix factorization algorithm for methylation modules (RNMF-MM) is presented, where the co-methylation constraint is treated as a regularizer. Using the artificial networks with known module structure, we demonstrate that the proposed algorithm outperforms state-of-the-art approaches in terms of accuracy. On the basis of breast cancer methylation data and protein interaction network, the RNMF-MM algorithm discovers methylation modules that are significantly more enriched by the known pathways than those obtained by other algorithms. These modules serve as biomarkers for predicting cancer stages and estimating survival time of patients. The proposed model and algorithm provide an effective way for the integrative analysis of protein interaction network and methylation data.

Our Buddies, Ourselves: The Role of Sexual Homophily in Adolescent Friendship Networks.

The present study tests the assumption that peers wield sufficient influence to induce sexual homophily (i.e., similarities in sexual experiences). Because girls face greater stigma for their sexual experiences than do boys, sexual homophily may be greater in girls' friendship networks than in boys'. Stochastic actor-based models were used to analyze network data (n=2,566; ages 14-18) from two high schools in the National Longitudinal Study of Adolescent to Adult Health. Sexual homophily was present in friendship networks. Girls and boys were equally susceptible to their friends' influence, but the former exhibited a stronger preference for befriending same sexual debut status peers than the latter. The findings suggest that adolescents-particularly girls-"curate" their networks to minimize peer ostracism.

Efficient simulation and analysis of mid-sized networks

Abstract There is growing interest in developing the abilities to simulate realistic social networks and analyze data generated from existing online social networks such as Facebook and Twitter. Amongst other things, researchers and practitioners need these abilities to study how opinions and information diffuse over networks and identify the influential agents in networks. However, the sizes of the social networks that need to be simulated and the amount of user generated data that needs to be analyzed is growing at a faster rate than the computational power of most of the modern day computers. This paper presents a memory efficient network representation and computational resource allocation algorithm that yields a scale-up of about 400; thus, given a constraint on the availability of computational resources, researchers can now use the proposed algorithm to simulate and analyze networks that are more than 100 times larger than what they could simulate otherwise. The proposed network representation is conducive to multi-core processing and random node sampling. Algorithms for computationally efficient execution of three random-node-sampling-based methods to estimate network metrics such as the network diameter and average path length are also presented in the paper. These algorithms yield a speed-up of about 40 even when the researcher requires a precision of more than 98%. The scale-up and speed-up numbers are based on a detailed performance analysis of the proposed algorithms that was conducted on synthetic networks of sizes ranging from 1000 to 1,000,000 nodes. The observed scale-up and speed-up performance of the proposed algorithms has been validated against the algorithms used in igraph and statnet-two popular network data analysis software package, and these results are also presented in this paper.

Real-Time Dynamic Network Anomaly Detection

Cybersecurity increasingly relies on the methodology used for statistical analysis of network data. The volume and velocity of enterprise network data sources puts a premium on streaming analytics that pass over the data once, while handling temporal variation in the process. In this paper we introduce ReTiNA: a framework for streaming network anomaly detection. This procedure first detects anomalies in the correlation processes on individual edges of the network graph. Second, anomalies across multiple edges are combined and scored to give network-wide situational awareness. The approach is tested in simulation and demonstrated on two real Netflow datasets.

Redes de capital social familiar en la región oriental de Jujuy (Argentina) durante las tres primeras décadas del siglo XX

In this research analyzes the utilization of the social capital category, and its possible linkages with the processes of accumulation relational and spatial mobility between extended family with multiple residences within the eastern region of the province of Jujuy. The main source of information with which it has developed work, comes from a set of baptismal records made between 1902 and 1935 by priests of the Prelature of Humahuaca, Jujuy, on the population living in the region known as Valle Grande. The results obtained after the ordering and the analysis of social networks (ARS) data entered in the baptismal records, give account of a series of relational feasible to be described under the category of family social capital.

Network(graph) data research in the coordinate system

Many approaches have been proposed to perform the analysis of network(graph) data correlated with Internet, social networks, communication networks, knowledge graph etc, but few of them have been applied in the coordinate system which is thought to be an efficient platform to processing graph data. In this survey, we provide a short yet structured analysis of network(graph) data research in the coordinate system. We first introduce the coordinate embedding and transforming of double-loop network with its symmetrical and regular structure. We then present two categories of approaches for Internet embedding in the coordinate system and the technology of graph embedding of social network. Finally, we draw our conclusions and discuss potential applications and future research direction.

Design and Implementation of E-Commerce Platform Based on Android

This paper introduces the basic functions of the e-commerce system implemented on Android, including user management functions, product search functions, product browsing functions, and product tracking functions, etc. It is necessary to use technologies in Android in order to implement each module function. For example, network communication requires Android’s network request technology and data analysis technology. The display of pictures requires the use of Android controls and cache technology. The traceability of products requires camera scanning technology. And RSA decryption technology and so on. Through the above thread pool technology and the use of caching mechanism, the user experience of UI will be improved and unnecessary network resources consumption will be avoided.

Network analysis of the Viking Age in Ireland as portrayed inCogadh Gaedhel re Gallaibh

Cogadh Gaedhel re Gallaibh (‘The War of the Gaedhil with the Gaill’) is a medieval Irish text, telling how an army under the leadership of Brian Boru challenged Viking invaders and their allies in Ireland, culminating with the Battle of Clontarf in 1014. Brian’s victory is widely remembered for breaking Viking power in Ireland, although much modern scholarship disputes traditional perceptions. Instead of an international conflict between Irish and Viking, interpretations based on revisionist scholarship consider it a domestic feud or civil war. Counter-revisionists challenge this view and a long-standing and lively debate continues. Here, we introduce quantitative measures to the discussions. We present statistical analyses of network data embedded in the text to position its sets of interactions on a spectrum from the domestic to the international. This delivers a picture that lies between antipodal traditional and revisionist extremes; hostilities recorded in the text are mostly between Irish and Viking—but internal conflict forms a significant proportion of the negative interactions too.

A Contribution of Media Façade Communication Design Strategy to the Local Community Based on Social Network Data Analysis

A Contribution of Media Façade Communication Design Strategy to the Local Community Based on Social Network Data Analysis

Hashtag #TBI: A content and network data analysis of tweets about Traumatic Brain Injury

ABSTRACTObjective: The aims of this study were to: (a) determine how Twitter is used by people with traumatic brain injury (TBI) and TBI organisations, (b) analyse the Twitter networks and content of tweets tagged with TBI-related hashtags, and (c) identify any challenges people with TBI encounter in using Twitter.Research Design: Mixed methods in a Twitter hashtag study.Methods: Mixed methods in a Twitter hashtag study. Tweets tagged with TBI-related hashtags were harvested from the Twitter website over a one-month period in 2016 and analysed qualitatively and quantitatively.Results: The sample of 29,199 tweets included tweets sent by 893 @users, 219 of whom had a brain injury. Twitter was used to: (a) discuss health issues, (b) raise awareness of TBI, (c) talk about life after TBI, (d) talk about sport and concussion, and (e) communicate inspirational messages.Conclusions: Twitter is an important platform for research and knowledge translation on TBI, and for hearing the voices of people with TBI as they express their personal views and stories of living with TBI and become more visible and influential in Twitter communities. TBI clinicians could use these narratives of people with TBI in Twitter to develop more effective and personally meaningful rehabilitation goals.

Sparse Network Completion via Discrete-Constrained Nuclear-Norm Minimization

In massive network data analysis, especially online social network analysis, the complete network dataset is often difficult to obtain due to the huge cost in collecting and storing data. In order to recover missing data from sampled networks, we consider the network completion problem, which has attracted much attention from both academia and industry. In this letter, the network completion problem of sparse networks is solved by a proposed discrete-constrained nuclear-norm minimization (DNM) method. It is based on the sparsity of the number of nonzero elements and singular values, which leads to an optimization problem. Since the problem is NP-hard, relaxation and adjustment are applied to make it convex. The DNM method can be applied in many practical networks, as many real-world complex networks are sparse. The simulation results on real-world online social networks and artificially generated random networks indicate that the proposed DNM method outperforms many existing network completion methods.

On the Sustainability of Co-Authoring Behaviors in Vietnamese Social Sciences: A Preliminary Analysis of Network Data

The topic of sustainability has mostly been tied to economic development and environmental protection, yet not much attention has been paid to the need for high problem-solving capacity as this underlines both issues. One of the most straightforward ways to improve problem-solving capacity is to nurture ‘productive and enduring’, i.e., sustainable, scientific communities. The study examines the co-authoring behaviors of 412 Vietnamese social scientists over the 2008–2017 period via social network analysis to determine if these researchers have formed sustainable scientific communities, using Scopus data. The dataset provides an insightful look into the predominant form of collaboration, i.e., co-authorship, within the Vietnamese social science research communities. Through basic network metrics such as density and clustering coefficient, the study hypothesizes that the socially sustainable research communities are those with low clustering and high density. As any scholar's position in a network can be specified by three quantities: the number of publications, connections, and years in research, the distance metrics from the most productive to the rest are computed and compared. The study hypothesizes that if the distance is too large; it reflects the socially unsustainable situation in the network. The results indicate that certain level of social unsustainability exists in social sciences groups in Vietnam. Though the results are only indicative, it has opened up a fertile space for future inquiry into this matter.

Machine learning meets complex networks via coalescent embedding in the hyperbolic space

Physicists recently observed that realistic complex networks emerge as discrete samples from a continuous hyperbolic geometry enclosed in a circle: the radius represents the node centrality and the angular displacement between two nodes resembles their topological proximity. The hyperbolic circle aims to become a universal space of representation and analysis of many real networks. Yet, inferring the angular coordinates to map a real network back to its latent geometry remains a challenging inverse problem. Here, we show that intelligent machines for unsupervised recognition and visualization of similarities in big data can also infer the network angular coordinates of the hyperbolic model according to a geometrical organization that we term “angular coalescence.” Based on this phenomenon, we propose a class of algorithms that offers fast and accurate “coalescent embedding” in the hyperbolic circle even for large networks. This computational solution to an inverse problem in physics of complex systems favors the application of network latent geometry techniques in disciplines dealing with big network data analysis including biology, medicine, and social science.

Diffusion, Seeding, and the Value of Network Information

Identifying the optimal set of individuals to first receive information (‘seeds’) in a social network is a widely-studied question in many settings, such as diffusion of information, spread of microfinance programs, and adoption of new technologies. Numerous studies have proposed various network-centrality based heuristics to choose seeds in a way that is likely to boost diffusion. Here we show that, for the classic SIR model of diffusion and some of its generalizations, randomly seeding s + x individuals can prompt a larger diffusion than optimally targeting the best s individuals, for a small x. We prove our results for large classes of random networks, and verify them in several small, real-world networks. Our results identify practically relevant settings under which collecting and analyzing network data to boost diffusion is not cost-effective.

Extraction and Analysis of Social Network Data Using Text Mining Techniques

Extraction and Analysis of Social Network Data Using Text Mining Techniques

Towards Visualizing Big Data with Large-Scale Edge Constraint Graph Drawing

Abstract Visualization plays an important role in enabling understanding of big data. Graphs are crucial tools for visual analytics of big data networks such as social, biological, traffic and security networks. Graph drawing has been intensively researched to enhance aesthetic features (i.e., layouts, symmetry, cross-free edges). Early physic-inspired techniques have focused on synthetic abstract graphs whose weights/distances of the edges are often ignored or assumed equal. Although recent approaches have been extended to sophisticated realistic networks, most are not designed to address very large-scale weighted graphs, which are important for visual analyses. The difficulty lies in the fact that the drawing process, governed by these physical properties, oscillates in large graphs and conflicts with specified distances leading to poor visual results. Our research attempts to alleviate these obstacles. This paper presents a simple graph visualization technique that aims to efficiently draw aesthetically pleasing large-scale straight-line weighted edge graphs. Our approach uses relevant physic-inspired techniques to promote aesthetic graphs and proposes a weak constraint-based approach to handle large-scale computing and competing goals to satisfy both weight requirements and aesthetic properties. The paper describes the approach along with experiments on both synthetic and real large-scale weighted graphs including that of over 10,000 nodes and comparisons with state-of-the-art approaches. The results obtained show enhanced and promising outcomes toward a general-purpose graph drawing technique for both big synthetic and real network data analytics.

Research on Organization of Mathematics Knowledge in Good Mathematical Cognitive Structure

This research adopted the method of network data analysis, chose 31 senior high school first-grade outstanding students as participants and 44 concepts about trigonometric function as materials, analyzed the organization of mathematics knowledge in good mathematical cognitive structure (GMCS) with the help of software Ucinet. The results indicated: (1) The connections between the concepts in GMCS were more extensive, especially those connections with higher tightness degree. (2) Most connections in GMCS were direct. (3) There were more abstract concepts as accumulation points connecting with others. (4) There were a number of concrete and frequently used concepts connecting with others directly. Therefore, the mathematics teachers should help students to construct extensive and direct connections between mathematics knowledge in their mind. These new findings expanded and deepened the current research about mathematical cognitive structure(MCS), pointed out the direction and target for educators helping their students form GMCS. Meanwhile, this research demonstrated the network data analysis method was feasible and valuable to analyze mathematics psychological issues.

Identification of miR-24 and miR-137 as novel candidate multiple sclerosis miRNA biomarkers using multi-staged data analysis protocol.

Many studies have investigated misregulation of miRNAs relevant to multiple sclerosis (MS) pathogenesis. Abnormal miRNAs can be used both as candidate biomarker for MS diagnosis and understanding the disease miRNA-mRNA regulatory network. In this comprehensive study, misregulated miRNAs related to MS were collected from existing literature, databases and via in silico prediction. A multi-staged data integration strategy (including the construction of miRNA-mRNA regulatory network and systematic data analysis) was conducted in order to investigate MS related miRNAs and their regulatory networks. The final outcome was a bi-layer MS related regulatory network constructed with 27 miRNAs (seven of them were novel) and 59 mRNA targets. To verify the accuracy of the bioinformatics strategy three novel and five previously reported miRNAs from the network model were selected for experimental validation using the real-time PCR assay. The obtained results proved the accuracy of the network. The expression of themiR-24 and miR-137(as novel MS candidate biomarker) and miR-16, and miR-181 (as previously reported MS candidate biomarker) showed significant deregulation in 33 MS patients compared to the control. The optimized data integration strategy conducted in this study found two miRNAs (miR-24and miR-16)that can be considered as candidate biomarkers for MS and also has the potential to generate a regulatory network to aid in further understanding the mechanisms underlying this disease.