scholarly journals Using data network metrics, graphics, and topology to explore network characteristics

2007 ◽  
pp. 62-75
Author(s):  
A. Adhikari ◽  
L. Denby ◽  
J. M. Landwehr ◽  
J. Meloche
Keyword(s):  
Data Network ◽  
Network Characteristics ◽  
And Topology ◽  
Using Data ◽  
Network Metrics

scholarly journals Blockchain Behavioral Traffic Model as a Tool to Influence Service IT Security

2020 ◽  
Vol 12 (4) ◽  
pp. 68 ◽  
Author(s):  
Vasiliy Elagin ◽  
Anastasia Spirkina ◽  
Andrei Levakov ◽  
Ilya Belozertsev
Keyword(s):  
Experimental Data ◽  
Present Article ◽  
Data Transmission ◽  
Behavioral Model ◽  
Traffic Model ◽  
It Security ◽  
Data Network ◽  
Network Characteristics

The present article describes the behavioral model of blockchain services; their reliability is confirmed on the basis of experimental data. The authors identify the main technical characteristics and features associated with data transmission through the network. The authors determine the network scheme, working with blockchain transactions and the dependence of network characteristics on application parameters. They analyze the application of this model for the detection of the blockchain service and the possibility of the existing security mechanisms of this technology being evaded. Furthermore, the article offers recommendations for hiding the blockchain traffic profile to significantly complicate its identification in the data network.

scholarly journals A Multi-Granularity Backbone Network Extraction Method Based on the Topology Potential

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Hanning Yuan ◽  
Yanni Han ◽  
Ning Cai ◽  
Wei An
Keyword(s):  
Network Connectivity ◽  
Self Similarity ◽  
Network Characteristics ◽  
The Public ◽  
Backbone Networks ◽  
Backbone Network ◽  
And Topology ◽  
Power Law Exponent ◽  
Network Compression ◽  
Public Datasets

Inspired by the theory of physics field, in this paper, we propose a novel backbone network compression algorithm based on topology potential. With consideration of the network connectivity and backbone compression precision, the method is flexible and efficient according to various network characteristics. Meanwhile, we define a metric named compression ratio to evaluate the performance of backbone networks, which provides an optimal extraction granularity based on the contributions of degree number and topology connectivity. We apply our method to the public available Internet AS network and Hep-th network, which are the public datasets in the field of complex network analysis. Furthermore, we compare the obtained results with the metrics of precision ratio and recall ratio. All these results show that our algorithm is superior to the compared methods. Moreover, we investigate the characteristics in terms of degree distribution and self-similarity of the extracted backbone. It is proven that the compressed backbone network has a lot of similarity properties to the original network in terms of power-law exponent.

scholarly journals Social Network Analysis of the Effects of a Social Media–Based Weight Loss Intervention Targeting Adults of Low Socioeconomic Status: Single-Arm Intervention Trial

10.2196/24690 ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. e24690
Author(s):  
Ran Xu ◽  
David Cavallo
Keyword(s):  
Social Support ◽  
Social Media ◽  
Weight Loss ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Self Efficacy ◽  
Web Based ◽  
Network Characteristics ◽  
Using Data

Background Obesity is a known risk factor for cardiovascular disease risk factors, including hypertension and type II diabetes. Although numerous weight loss interventions have demonstrated efficacy, there is considerably less evidence about the theoretical mechanisms through which they work. Delivering lifestyle behavior change interventions via social media provides unique opportunities for understanding mechanisms of intervention effects. Server data collected directly from web-based platforms can provide detailed, real-time behavioral information over the course of intervention programs that can be used to understand how interventions work. Objective The objective of this study was to demonstrate how social network analysis can facilitate our understanding of the mechanisms underlying a social media–based weight loss intervention. Methods We performed secondary analysis by using data from a pilot study that delivered a dietary and physical activity intervention to a group of participants via Facebook. We mapped out participants’ interaction networks over the 12-week intervention period and linked participants’ network characteristics (eg, in-degree, out-degree, network constraint) to participants’ changes in theoretical mediators (ie, dietary knowledge, perceived social support, self-efficacy) and weight loss by using regression analysis. We also performed mediation analyses to explore how the effects of social network measures on weight loss could be mediated by the aforementioned theoretical mediators. Results In this analysis, 47 participants from 2 waves completed the study and were included. We found that increases in the number of posts, comments, and reactions significantly predicted weight loss (β=–.94, P=.04); receiving comments positively predicted changes in self-efficacy (β=7.81, P=.009), and the degree to which one’s network neighbors are tightly connected with each other weakly predicted changes in perceived social support (β=7.70, P=.08). In addition, change in self-efficacy mediated the relationship between receiving comments and weight loss (β=–.89, P=.02). Conclusions Our analyses using data from this pilot study linked participants’ network characteristics with changes in several important study outcomes of interest such as self-efficacy, social support, and weight. Our results point to the potential of using social network analysis to understand the social processes and mechanisms through which web-based behavioral interventions affect participants’ psychological and behavioral outcomes. Future studies are warranted to validate our results and to further explore the relationship between network dynamics and study outcomes in similar and larger trials.

scholarly journals Heritability in friendship networks

2020 ◽  
Author(s):  
Michael Neugart ◽  
Selen Yildirim
Keyword(s):  
Friendship Networks ◽  
Economic Success ◽  
Twin Design ◽  
Network Characteristics ◽  
Classical Twin Design ◽  
Using Data

AbstractFriendship networks account for a large part of an individual’s economic success or failure in life. Using data from the German TwinLife study, we explore, within a classical twin design, to which extent friendship networks are related to genes. We find a substantial heritability component in twins’ network sizes and network homophily, but not in twins’ network closeness. Addressing indirect ways in which genes could influence network characteristics, we do not find evidence that shared hobbies affects networks.

scholarly journals Using Social Network Analysis to Understand the Effects of a Social Media-based Weight Loss Intervention Targeting Low-SES Adults (Preprint)

2020 ◽  
Author(s):  
Ran Xu ◽  
David Cavallo
Keyword(s):  
Social Support ◽  
Social Media ◽  
Weight Loss ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Self Efficacy ◽  
Network Characteristics ◽  
Using Data ◽  
The Relationship

BACKGROUND Obesity is a known risk factor for cardiovascular disease (CVD) risk factors including hypertension and type II diabetes. Although numerous weight-loss interventions have demonstrated efficacy, there is considerably less evidence about the theoretical mechanisms through which they work. Delivering lifestyle behavior change interventions via social media provides unique opportunities for understanding mechanisms of intervention effects. Server data collected directly from online platforms can provide detailed, real-time behavioral information over the course of intervention programs that can be used to understand how interventions work. OBJECTIVE The objective of this study was to demonstrate how social network analysis can facilitate our understanding of the mechanisms underlying a social-media based weight loss intervention. METHODS This study performed secondary analysis using data from a pilot study that delivered a dietary and physical activity intervention to a group of low-SES participants via Facebook. We mapped out participants’ interaction networks over the 12-week intervention period, and linked participants’ network characteristics (e.g. in-degree, out-degree and network constraint) to participants’ changes in theoretical mediators (i.e. dietary knowledge, perceived social support, self-efficacy) and weight loss using regression analysis. This study also performed mediation analyses to explore how the effects of social network measures on weight loss could be mediated by the aforementioned theoretical mediators. RESULTS 47 participants from two waves completed the study and were included in the analysis. We found that participants creating posts, comments and reactions predicted weight-loss (β=-.94, P=.042); receiving comments positively predicted changes in self-efficacy (β=7.81, P=.009); the degree to which one’s network neighbors are tightly connected with each other weakly predicted changes in perceived social support (β=7.70, P=.08). In addition, change in self-efficacy mediated the relationship between receiving comments and weight-loss (Indirect effect=-.89, P=.017). CONCLUSIONS Our analyses using data from this pilot study have linked participants’ network characteristics with changes in several important study outcomes of interest, such as self-efficacy, social support and weight. Our results point to the potential of using social network analysis to understand the social processes and mechanisms through which online behavioral interventions affects participants’ psychological and behavioral outcomes. Future studies are warranted to validate our results and further explore the relationship between network dynamics and study outcomes in similar and larger trials.

scholarly journals The influence of dependence on data network models

2008 ◽  
Vol 40 (1) ◽  
pp. 60-94 ◽  
Author(s):  
Bernardo D'Auria ◽  
Sidney I. Resnick
Keyword(s):  
Transmission Rate ◽  
Network Models ◽  
Dependence Structure ◽  
End User ◽  
Independence Assumption ◽  
Data Network ◽  
Network Characteristics ◽  
Network Applications ◽  
Peer To Peer Networks ◽  
Consecutive Time

Consider an infinite-source marked Poisson process to model end user inputs to a data network. At Poisson times, connections are initated. The connection is characterized by a triple (F, L, R) denoting the total quantity of transmitted data in a connection, the length or duration of the connection, and the transmission rate; the three quantities are related by F = LR. How critical is the dependence structure of the mark for network characteristics such as burstiness, distribution tails of cumulative input, and long-range dependence properties of traffic measured in consecutive time slots? In a previous publication (D'Auria and Resnick (2006)) we assumed that F and R were independent. Here we assume that L and R are independent. The change in dependence assumptions means that the model properties change dramatically: tails of cumulative input per time slot are dramatically heavier, traffic cannot be approximated by a Gaussian distribution, and the decay of dependence cannot be measured in the traditional way using correlation functions. Different network applications are likely to have different mark dependence structure. We argue that the present independence assumption on L and R is likely to be appropriate for network applications such as streaming media or peer-to-peer networks. Our conclusion is that it is desirable to separate network traffic by application and to model each application with its own appropriate dependence structure.

scholarly journals Socioeconomic Status, Time Scarcity and Well-Being in Retirement

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 913-913
Author(s):  
Boroka Bo
Keyword(s):  
Socioeconomic Status ◽  
Life Experience ◽  
Participant Observation ◽  
Later Life ◽  
Well Being ◽  
Network Characteristics ◽  
Experience Of Time ◽  
Peer Network ◽  
Using Data ◽  
Cumulative Inequality

Abstract We tend to think of retirement as a great equalizer when it comes to relief from the pernicious time scarcity characterizing the lives of many individuals in the labor force. Puzzlingly, this is not entirely the case. Using data from the MTUS (N=15,390) in combination with long-term participant observation (980 hours) and in-depth interviews (N=53), I show that socioeconomic characteristics are important determinants of retiree time scarcity. Neighborhood disadvantage gets under the skin via time exchanges that are forged by both neighborhood and peer network characteristics. The SES-based ‘time projects of surviving and thriving’ undergirding the experience of time scarcity lead to divergent strategies of action and differing consequences for well-being. For the advantaged, the experience of time scarcity is protective for well-being in later life, as it emerges from the ‘work of thriving’ and managing a relative abundance of choices. For the disadvantaged, the later life experience of time scarcity is shaped by cumulative inequality, further exacerbating inequalities in well-being. The final section of the article offers an analysis and interpretation of these results, putting retiree time scarcity in conversation with the broader literature on socioeconomic status and well-being.

scholarly journals Association between network characteristics and bicycle ridership across a large metropolitan region

2021 ◽  
Author(s):  
Ben Beck ◽  
Christopher Pettit ◽  
Meghan Winters ◽  
Trisalyn Nelson ◽  
Hai Vu ◽  
...  
Keyword(s):  
Spatial Models ◽  
Monitoring And Evaluation ◽  
Metropolitan Region ◽  
Metropolitan Regions ◽  
Network Characteristics ◽  
Household Travel Survey ◽  
Bicycle Infrastructure ◽  
Household Travel ◽  
Bayesian Spatial Models ◽  
Network Metrics

Background: Numerous studies have explored associations between bicycle network characteristics and bicycle ridership. However, the majority of these studies have been conducted in inner metropolitan regions and as such, there is limited knowledge on how various characteristics of bicycle networks relate to bicycle trips within and across entire metropolitan regions, and how the size and composition of study regions impact on the association between bicycle network characteristics and bicycle ridership.Methods: We conducted a retrospective analysis of household travel survey data and bicycle infrastructure in the Greater Melbourne region, Australia. Seven network metrics were calculated and Bayesian spatial models were used to explore the association between these network characteristics and bicycle ridership (measured as counts of the number of trips, and the proportion of all trips that were made by bike). Results: We demonstrated that bicycle ridership was associated with several network characteristics, and that these characteristics varied according to the outcome (count of the number of trips made by bike or the proportion of trips made by bike) and the size and characteristics of the study region.Conclusions: These findings challenge the utility of approaches based on spatially modelling network characteristics and bicycle ridership when informing the monitoring and evaluation of bicycle networks. There is a need to progress the science of measuring safe and connected bicycle networks for people of all ages and abilities.

Decision-Making in Collaborative Governance Networks: Pathways to Input and Throughput Legitimacy

2020 ◽  
Author(s):  
Jennifer E Mosley ◽  
Jade Wong
Keyword(s):  
Decision Making ◽  
Qualitative Comparative Analysis ◽  
Collaborative Governance ◽  
Comparative Case Study ◽  
Network Characteristics ◽  
Governance Networks ◽  
Decision Making Processes ◽  
Using Data ◽  
Governance Processes ◽  
Input Legitimacy

Abstract Participants may lose faith in collaborative governance processes if they do not perceive internal decision-making processes to be legitimate. Yet, understanding how to assess internal legitimacy and what network characteristics are associated with it has been an enduring challenge. In this article, we propose conceptualizing internal legitimacy as multi-vectored, contrasting input legitimacy—the degree of openness and access that participants experience in their attempt to offer voice—with throughput legitimacy—the quality of the decision-making process itself. Using data from a comparative case study of 18 different US Department of Housing and Urban Development (HUD)-mandated Continuums of Care, we assess this framework with a mixed-methods approach, combining thematic analysis of interview data (n = 145) with Qualitative Comparative Analysis (QCA) to show (1) differences in how participants experience input and throughput legitimacy, (2) the nature of the relationship between input and throughput legitimacy, and (3) what specific network characteristics are associated with positive assessments of each. Our findings indicate that input and throughput legitimacy are distinct but related—throughput legitimacy is harder to achieve and dependent on positive assessments of input legitimacy. Some network characteristics, particularly large size and commissioner-style network management, pose challenges, but a focus on in-person engagement can help ameliorate them. We conclude that distinguishing between input and throughput legitimacy can help managers identify where and how to intervene in order to improve the legitimacy of decision-making processes in collaborative governance networks.

Sign in / Sign up

Export Citation Format

Share Document