Network Science最新文献_第9页

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

Learning to count: A deep learning framework for graphlet count estimation 学习计数:一个用于graphlet计数估计的深度学习框架

IF 1.7

Network Science Pub Date : 2020-09-11 DOI: 10.1017/nws.2020.35

Xutong Liu, Y. Chen, John C.S. Lui, Konstantin Avrachenkov

{"title":"Learning to count: A deep learning framework for graphlet count estimation","authors":"Xutong Liu, Y. Chen, John C.S. Lui, Konstantin Avrachenkov","doi":"10.1017/nws.2020.35","DOIUrl":"https://doi.org/10.1017/nws.2020.35","url":null,"abstract":"Abstract Graphlet counting is a widely explored problem in network analysis and has been successfully applied to a variety of applications in many domains, most notatbly bioinformatics, social science, and infrastructure network studies. Efficiently computing graphlet counts remains challenging due to the combinatorial explosion, where a naive enumeration algorithm needs O(Nk) time for k-node graphlets in a network of size N. Recently, many works introduced carefully designed combinatorial and sampling methods with encouraging results. However, the existing methods ignore the fact that graphlet counts and the graph structural information are correlated. They always consider a graph as a new input and repeat the tedious counting procedure on a regular basis even if it is similar or exactly isomorphic to previously studied graphs. This provides an opportunity to speed up the graphlet count estimation procedure by exploiting this correlation via learning methods. In this paper, we raise a novel graphlet count learning (GCL) problem: given a set of historical graphs with known graphlet counts, how to learn to estimate/predict graphlet count for unseen graphs coming from the same (or similar) underlying distribution. We develop a deep learning framework which contains two convolutional neural network models and a series of data preprocessing techniques to solve the GCL problem. Extensive experiments are conducted on three types of synthetic random graphs and three types of real-world graphs for all 3-, 4-, and 5-node graphlets to demonstrate the accuracy, efficiency, and generalizability of our framework. Compared with state-of-the-art exact/sampling methods, our framework shows great potential, which can offer up to two orders of magnitude speedup on synthetic graphs and achieve on par speed on real-world graphs with competitive accuracy.","PeriodicalId":51827,"journal":{"name":"Network Science","volume":"9 1","pages":"S23 - S60"},"PeriodicalIF":1.7,"publicationDate":"2020-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/nws.2020.35","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43542694","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

Gradient and Harnack-type estimates for PageRank PageRank的梯度和harnack型估计

IF 1.7

Network Science Pub Date : 2020-09-03 DOI: 10.1017/nws.2020.34

P. Horn, Lauren M. Nelsen

引用次数: 2

Mobilizing nascent ties: A Qualitative Structural Analysis of social(izing) capital in newcomer networks 动员新生关系:新来者网络中社会(化)资本的定性结构分析

IF 1.7

Network Science Pub Date : 2020-09-01 DOI: 10.1017/nws.2020.25

Sabine R. Bakker

{"title":"Mobilizing nascent ties: A Qualitative Structural Analysis of social(izing) capital in newcomer networks","authors":"Sabine R. Bakker","doi":"10.1017/nws.2020.25","DOIUrl":"https://doi.org/10.1017/nws.2020.25","url":null,"abstract":"Abstract This paper investigates the processes involved when newly hired employees need to simultaneously build up and mobilize personal network ties during their organizational socialization. It focuses on the quality of ties at an early formative stage, characterized by the lack of a tie history between actors. Social capital theory would suggest that such nascent ties do not offer optimal channels for the kind and volume of resources that newcomers (need to) rely on during socialization. To better understand how this apparent mismatch between tie quality and resource needs is handled from an ego-centered perspective, the paper analyzes personal network data from 24 newcomers in nine organizations, using an adapted form of Qualitative Structural Analysis. Three tie-level qualities are found to explain how the lack of tie history may be alleviated, circumvented, or compensated. They comprise (a) variants of openness experienced with stronger ties, (b) perceptions of a lowered threshold towards weaker ties, and (c) sources of legitimacy regarding latent ties. Based on these findings, the paper presents an integrated conceptual model to clarify how nascent ties offer channels for network resources during socialization and discusses the need for further research on the role of specific moderators for the investigated processes.","PeriodicalId":51827,"journal":{"name":"Network Science","volume":"8 1","pages":"381 - 398"},"PeriodicalIF":1.7,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/nws.2020.25","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48483077","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: 1

Political isolation in America 美国的政治孤立

IF 1.7

Network Science Pub Date : 2020-09-01 DOI: 10.1017/nws.2020.9

Byungkyu Lee, P. Bearman

引用次数: 9

The networked question in the digital era: How do networked, bounded, and limited individuals connect at different stages in the life course? 数字时代的网络化问题:网络化的、有界的和有限的个体在生命历程的不同阶段是如何联系在一起的?

IF 1.7

Network Science Pub Date : 2020-09-01 DOI: 10.1017/nws.2019.28

B. Wellman, Anabel Quan-Haase, Molly-Gloria Harper

{"title":"The networked question in the digital era: How do networked, bounded, and limited individuals connect at different stages in the life course?","authors":"B. Wellman, Anabel Quan-Haase, Molly-Gloria Harper","doi":"10.1017/nws.2019.28","DOIUrl":"https://doi.org/10.1017/nws.2019.28","url":null,"abstract":"Abstract We used in-depth interviews with 101 participants in the East York section of Toronto, Canada to understand how digital media affects social connectivity in general—and networked individualism in particular—for people at different stages of the life course. Although people of all ages intertwined their use of digital media with their face-to-face interactions, younger adults used more types of digital media and have more diversified personal networks. People in different age-groups conserved media, tending to stick with the digital media they learned to use in earlier life stages. Approximately one-third of the participants were Networked Individuals: In each age-group, they were the most actively using digital media to maintain ties and to develop new ones. Another one-third were Socially Bounded, who often actively used digital media but kept their connectivity within a smaller set of social groups. The remaining one-third, who were Socially Limited, were the least likely to use digital media. Younger adults were the most likely to be Networked Individuals, leading us to wonder if the percentage of the population who are Bounded or Limited will decline over time.","PeriodicalId":51827,"journal":{"name":"Network Science","volume":"8 1","pages":"291 - 312"},"PeriodicalIF":1.7,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/nws.2019.28","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43499321","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: 10

Egonets as systematically biased windows on society 自我是有系统偏见的社会窗口

IF 1.7

Network Science Pub Date : 2020-09-01 DOI: 10.1017/nws.2020.5

S. Feld, Alec McGail

{"title":"Egonets as systematically biased windows on society","authors":"S. Feld, Alec McGail","doi":"10.1017/nws.2020.5","DOIUrl":"https://doi.org/10.1017/nws.2020.5","url":null,"abstract":"Abstract A person’s egonet, the set of others with whom that person is connected, is a personal sample of society which especially influences that person’s experience and perceptions of society. We show that egonets systematically misrepresent the general population because each person is included in as many egonets as that person has “friends.” Previous research has recognized that this unequal weighting in egonets leads many people to find that their friends have more friends than they themselves have. This paper builds upon that research to show that people’s egonets provide them with systematically biased samples of the population more generally. We discuss how this ubiquitous egonet bias may have far reaching implications for people’s experiences and perceptions of frequencies of other people’s ties and traits in ways that may influence their own feelings and behaviors. In particular, these egonet biases may help explain people’s tendencies to disproportionately experience and overestimate the prevalence of certain types of deviance and other social behaviors and consequently be influenced toward them. We illustrate egonet bias with analyses of all friends among 63,731 Facebook users. We call for further empirical investigation of egonet biases and their consequences for individuals and society.","PeriodicalId":51827,"journal":{"name":"Network Science","volume":"8 1","pages":"399 - 417"},"PeriodicalIF":1.7,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/nws.2020.5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44734968","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

Where you are, what you want, and what you can do: The role of master statuses, personality traits, and social cognition in shaping ego network size, structure, and composition 你在哪里，你想要什么，你能做什么:主体性地位、人格特质和社会认知在塑造自我网络规模、结构和组成中的作用

IF 1.7

Network Science Pub Date : 2020-09-01 DOI: 10.1017/nws.2020.6

Matthew E. Brashears, Laura Aufderheide Brashears, Nicolas L. Harder

引用次数: 3