{"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}
引用次数: 0
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
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.
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