Network Science最新文献_第9页

A network approach to measuring state preferences 衡量国家偏好的网络方法

IF 1.7

Network Science Pub Date : 2021-01-20 DOI: 10.1017/nws.2020.44

Max Gallop, Shahryar Minhas

{"title":"A network approach to measuring state preferences","authors":"Max Gallop, Shahryar Minhas","doi":"10.1017/nws.2020.44","DOIUrl":"https://doi.org/10.1017/nws.2020.44","url":null,"abstract":"Abstract State preferences play an important role in international politics. Unfortunately, actually observing and measuring these preferences are impossible. In general, scholars have tried to infer preferences using either UN voting or alliance behavior. The two most notable measures of state preferences that have flowed from this research area are ideal points (Bailey et al., 2017) and S-scores (Signorino & Ritter, 1999). The basis of both these models is a spatial weighting scheme that has proven useful but discounts higher-order effects that might be present in relational data structures such as UN voting and alliances. We begin by arguing that both alliances and UN voting are simply examples of the multiple layers upon which states interact with one another. To estimate a measure of state preferences, we utilize a tensor decomposition model that provides a reduced-rank approximation of the main patterns across the layers. Our new measure of preferences plausibly describes important state relations and yields important insights on the relationship between preferences, democracy, and international conflict. Additionally, we show that a model of conflict using this measure of state preferences decisively outperforms models using extant measures when it comes to predicting conflict in an out-of-sample context.","PeriodicalId":51827,"journal":{"name":"Network Science","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2021-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/nws.2020.44","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48686916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

A pairwise strategic network formation model with group heterogeneity: With an application to international travel 具有群体异质性的成对战略网络形成模型及其在国际旅行中的应用

IF 1.7

Network Science Pub Date : 2020-12-29 DOI: 10.1017/nws.2022.16

Tadao Hoshino

引用次数: 1

Imitation, network size, and efficiency 模仿、网络规模和效率

IF 1.7

Network Science Pub Date : 2020-12-04 DOI: 10.1017/nws.2020.43

Carlos Alós-Ferrer, J. Buckenmaier, F. Farolfi

引用次数: 2

Dynamic Network Prediction. 动态网络预测。

IF 1.7

Network Science Pub Date : 2020-12-01 Epub Date: 2020-07-09 DOI: 10.1017/nws.2020.24

Ravi Goyal Mathematica, Victor De Gruttola

{"title":"Dynamic Network Prediction.","authors":"Ravi Goyal Mathematica, Victor De Gruttola","doi":"10.1017/nws.2020.24","DOIUrl":"https://doi.org/10.1017/nws.2020.24","url":null,"abstract":"We present a statistical framework for generating predicted dynamic networks based on the observed evolution of social relationships in a population. The framework includes a novel and flexible procedure to sample dynamic networks given a probability distribution on evolving network properties; it permits the use of a broad class of approaches to model trends, seasonal variability, uncertainty, and changes in population composition. Current methods do not account for the variability in the observed historical networks when predicting the network structure; the proposed method provides a principled approach to incorporate uncertainty in prediction. This advance aids in the designing of network-based interventions, as development of such interventions often requires prediction of the network structure in the presence and absence of the intervention. Two simulation studies are conducted to demonstrate the usefulness of generating predicted networks when designing network-based interventions. The framework is also illustrated by investigating results of potential interventions on bill passage rates using a dynamic network that represents the sponsor/co-sponsor relationships among senators derived from bills introduced in the US Senate from 2003-2016.","PeriodicalId":51827,"journal":{"name":"Network Science","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/nws.2020.24","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33444817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

The structure of emotional support networks in families affected by Lynch syndrome. 受林奇综合征影响家庭的情感支持网络结构。

IF 1.7

Network Science Pub Date : 2020-12-01 Epub Date: 2020-04-24 DOI: 10.1017/nws.2020.13

Christopher Steven Marcum, Dawn Lea, Dina Eliezer, Donald W Hadley, Laura M Koehly

{"title":"The structure of emotional support networks in families affected by Lynch syndrome.","authors":"Christopher Steven Marcum, Dawn Lea, Dina Eliezer, Donald W Hadley, Laura M Koehly","doi":"10.1017/nws.2020.13","DOIUrl":"10.1017/nws.2020.13","url":null,"abstract":"Genetic risk is particularly salient for families and testing for genetic conditions is necessarily a family-level process. Thus, risk for genetic disease represents a collective stressor shared by family members. According to communal coping theory, families may adapt to such risk vis-a-vis interpersonal exchange of support resources. We propose that communal coping is operationalized through the pattern of supportive relationships observed between family members. In this study, we take a social network perspective to map communal coping mechanisms to their underlying social interactions and include those who declined testing or were not at risk for Lynch Syndrome. Specifically, we examine the exchange of emotional support resources in families at risk of Lynch Syndrome, a dominantly inherited cancer susceptibility syndrome. Our results show that emotional support resources depend on the testing-status of individual family members and are not limited to the bounds of the family. Network members from within and outside the family system are an important coping resource in this patient population. This work illustrates how social network approaches can be used to test structural hypotheses related to communal coping within a broader system and identifies structural features that characterize coping processes in families affected by Lynch Syndrome.","PeriodicalId":51827,"journal":{"name":"Network Science","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7995833/pdf/nihms-1579881.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25525974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

NWS volume 8 issue 4 Cover and Back matter 国家气象局第8卷第4期封面和封底

IF 1.7

Network Science Pub Date : 2020-12-01 DOI: 10.1017/nws.2020.41

引用次数: 0

NWS volume 8 issue 4 Cover and Front matter NWS第8卷第4期封面和封面

IF 1.7

Network Science Pub Date : 2020-12-01 DOI: 10.1017/nws.2020.40

引用次数: 0

On the impact of network size and average degree on the robustness of centrality measures 网络规模和平均度对中心性测度稳健性的影响

IF 1.7

Network Science Pub Date : 2020-10-20 DOI: 10.1017/nws.2020.37

Christoph Martin, Peter Niemeyer

{"title":"On the impact of network size and average degree on the robustness of centrality measures","authors":"Christoph Martin, Peter Niemeyer","doi":"10.1017/nws.2020.37","DOIUrl":"https://doi.org/10.1017/nws.2020.37","url":null,"abstract":"Abstract Measurement errors are omnipresent in network data. Most studies observe an erroneous network instead of the desired error-free network. It is well known that such errors can have a severe impact on network metrics, especially on centrality measures: a central node in the observed network might be less central in the underlying, error-free network. The robustness is a common concept to measure these effects. Studies have shown that the robustness primarily depends on the centrality measure, the type of error (e.g., missing edges or missing nodes), and the network topology (e.g., tree-like, core-periphery). Previous findings regarding the influence of network size on the robustness are, however, inconclusive. We present empirical evidence and analytical arguments indicating that there exist arbitrary large robust and non-robust networks and that the average degree is well suited to explain the robustness. We demonstrate that networks with a higher average degree are often more robust. For the degree centrality and Erdős–Rényi (ER) graphs, we present explicit formulas for the computation of the robustness, mainly based on the joint distribution of node degrees and degree changes which allow us to analyze the robustness for ER graphs with a constant average degree or increasing average degree.","PeriodicalId":51827,"journal":{"name":"Network Science","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2020-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/nws.2020.37","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47438776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Sensitivity analysis for network observations with applications to inferences of social influence effects 网络观测的敏感性分析及其在社会影响效应推断中的应用

IF 1.7

Network Science Pub Date : 2020-10-19 DOI: 10.1017/nws.2020.36

Ran Xu, K. Frank

{"title":"Sensitivity analysis for network observations with applications to inferences of social influence effects","authors":"Ran Xu, K. Frank","doi":"10.1017/nws.2020.36","DOIUrl":"https://doi.org/10.1017/nws.2020.36","url":null,"abstract":"Abstract The validity of network observations is sometimes of concern in empirical studies, since observed networks are prone to error and may not represent the population of interest. This lack of validity is not just a result of random measurement error, but often due to systematic bias that can lead to the misinterpretation of actors’ preferences of network selections. These issues in network observations could bias the estimation of common network models (such as those pertaining to influence and selection) and lead to erroneous statistical inferences. In this study, we proposed a simulation-based sensitivity analysis method that can evaluate the robustness of inferences made in social network analysis to six forms of selection mechanisms that can cause biases in network observations—random, homophily, anti-homophily, transitivity, reciprocity, and preferential attachment. We then applied this sensitivity analysis to test the robustness of inferences for social influence effects, and we derived two sets of analytical solutions that can account for biases in network observations due to random, homophily, and anti-homophily selection.","PeriodicalId":51827,"journal":{"name":"Network Science","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2020-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/nws.2020.36","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41844677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Isolation concepts applied to temporal clique enumeration 孤立概念在时间集团枚举中的应用

IF 1.7

Network Science Pub Date : 2020-10-16 DOI: 10.1017/nws.2020.38

Hendrik Molter, R. Niedermeier, Malte Renken

引用次数: 4