Network Science最新文献_第8页

Sampling methods and estimation of triangle count distributions in large networks 大型网络中三角形计数分布的采样方法和估计

IF 1.7

Network Science Pub Date : 2021-02-26 DOI: 10.1017/nws.2021.2

Nelson Antunes, Tianjian Guo, V. Pipiras

引用次数: 6

NWS volume 9 issue 1 Cover and Back matter NWS第9卷第1期封面和封底

IF 1.7

Network Science Pub Date : 2021-02-04 DOI: 10.1017/nws.2020.47

manuel muñoz-herrera, rafael wittek

{"title":"NWS volume 9 issue 1 Cover and Back matter","authors":"manuel muñoz-herrera, rafael wittek","doi":"10.1017/nws.2020.47","DOIUrl":"https://doi.org/10.1017/nws.2020.47","url":null,"abstract":"original Articles Collaborative production networks among unequal actors manuel muñoz-herrera, jacob dijkstra, andreas flache and rafael wittek 1 Social network change after moving into permanent supportive housing: Who stays and who goes? harmony rhoades, hsun-ta hsu, eric rice, taylor harris, wichada la motte-kerr, hailey winetrobe, benjamin henwood and suzanne wenzel 18 Social cohesion emerging from a community-based physical activity program: A temporal network analysis ana maría jaramillo, felipe montes, olga l. sarmiento, ana paola ríos, lisa g. rosas, ruth f. hunter, ana lucía rodríguez and abby c. king 35 Superbubbles as an empirical characteristic of directed networks fabian gärtner, felix kühnl, carsten r. seemann, the students of the graphs and networks computer lab 2018/19, christian höner zu siederdissen and peter f. stadler 49 Single-seed cascades on clustered networks john k. mcsweeney 59 Sensitivity analysis for network observations with applications to inferences of social influence effects ran xu and kenneth a. frank 73 Analysis of population functional connectivity data via multilayer network embeddings james d.wilson, melanie baybay, rishi sankar, paul stillman and abbie m. popa 99 Imitation, network size, and efficiency carlos alós-ferrer, johannes buckenmaier and federica farolfi 123 network science editorial team","PeriodicalId":51827,"journal":{"name":"Network Science","volume":" ","pages":"b1 - b2"},"PeriodicalIF":1.7,"publicationDate":"2021-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/nws.2020.47","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48184434","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

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":"9 1","pages":"135 - 152"},"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

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":"9 1","pages":"S61 - S82"},"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":"9 1","pages":"73 - 98"},"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