NWS第九卷第S1期封面和封底

IF 1.4 Q2 SOCIAL SCIENCES, INTERDISCIPLINARY

Network Science Pub Date : 2021-09-08 DOI:10.1017/nws.2021.14

xutong liu

{"title":"NWS第九卷第S1期封面和封底","authors":"xutong liu","doi":"10.1017/nws.2021.14","DOIUrl":null,"url":null,"abstract":"original Articles Gradient and Harnack-type estimates for PageRank paul horn and lauren m. nelsen S4 Learning to count: A deep learning framework for graphlet count estimation xutong liu, yu-zhen janice chen, john c. s. lui and konstantin avrachenkov S23 On the impact of network size and average degree on the robustness of centrality measures christoph martin and peter niemeyer S61 Isolation concepts applied to temporal clique enumeration hendrik molter, rolf niedermeier and malte renken S83 A simple differential geometry for complex networks emil saucan, areejit samal and jürgen jost S106 Sampling methods and estimation of triangle count distributions in large networks nelson antunes, tianjian guo and vladas pipiras S134 Logic and learning in network cascades galen j.wilkerson and sotiris moschoyiannis S157 network science editorial team","PeriodicalId":51827,"journal":{"name":"Network Science","volume":null,"pages":null},"PeriodicalIF":1.4000,"publicationDate":"2021-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"NWS volume 9 issue S1 Cover and Back matter\",\"authors\":\"xutong liu\",\"doi\":\"10.1017/nws.2021.14\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"original Articles Gradient and Harnack-type estimates for PageRank paul horn and lauren m. nelsen S4 Learning to count: A deep learning framework for graphlet count estimation xutong liu, yu-zhen janice chen, john c. s. lui and konstantin avrachenkov S23 On the impact of network size and average degree on the robustness of centrality measures christoph martin and peter niemeyer S61 Isolation concepts applied to temporal clique enumeration hendrik molter, rolf niedermeier and malte renken S83 A simple differential geometry for complex networks emil saucan, areejit samal and jürgen jost S106 Sampling methods and estimation of triangle count distributions in large networks nelson antunes, tianjian guo and vladas pipiras S134 Logic and learning in network cascades galen j.wilkerson and sotiris moschoyiannis S157 network science editorial team\",\"PeriodicalId\":51827,\"journal\":{\"name\":\"Network Science\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.4000,\"publicationDate\":\"2021-09-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Network Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1017/nws.2021.14\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"SOCIAL SCIENCES, INTERDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Network Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1017/nws.2021.14","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"SOCIAL SCIENCES, INTERDISCIPLINARY","Score":null,"Total":0}

引用次数: 0

摘要

原创文章PageRank的梯度和Harnack类型估计paul horn和lauren m.nelsen S4学习计数：一个用于graphlet计数估计的深度学习框架xutong liu，yu zhen janice chen，john c.s.lui和konstantin avrachenkov S23关于网络大小和平均程度对中心性测度稳健性的影响christoph martin和peter niemeyer S61应用于时间团枚举的孤立概念hendrik molter、rolf niedermeier和malte renken S83复杂网络的简单微分几何emil saucan，areejit samal和jürgen jost S106大型网络中三角形计数分布的采样方法和估计nelson antunes，tianjian guo和vladas pipiras S134网络级联中的逻辑和学习galen j.wilkerson和sotiris moschoyiannis S157网络科学编辑团队

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

NWS volume 9 issue S1 Cover and Back matter

original Articles Gradient and Harnack-type estimates for PageRank paul horn and lauren m. nelsen S4 Learning to count: A deep learning framework for graphlet count estimation xutong liu, yu-zhen janice chen, john c. s. lui and konstantin avrachenkov S23 On the impact of network size and average degree on the robustness of centrality measures christoph martin and peter niemeyer S61 Isolation concepts applied to temporal clique enumeration hendrik molter, rolf niedermeier and malte renken S83 A simple differential geometry for complex networks emil saucan, areejit samal and jürgen jost S106 Sampling methods and estimation of triangle count distributions in large networks nelson antunes, tianjian guo and vladas pipiras S134 Logic and learning in network cascades galen j.wilkerson and sotiris moschoyiannis S157 network science editorial team

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Network Science SOCIAL SCIENCES, INTERDISCIPLINARY-

CiteScore

3.50

自引率

5.90%

发文量

期刊介绍： Network Science is an important journal for an important discipline - one using the network paradigm, focusing on actors and relational linkages, to inform research, methodology, and applications from many fields across the natural, social, engineering and informational sciences. Given growing understanding of the interconnectedness and globalization of the world, network methods are an increasingly recognized way to research aspects of modern society along with the individuals, organizations, and other actors within it. The discipline is ready for a comprehensive journal, open to papers from all relevant areas. Network Science is a defining work, shaping this discipline. The journal welcomes contributions from researchers in all areas working on network theory, methods, and data.