{"title":"网络类和图复杂度度量","authors":"M. Dehmer, Stephan Borgert, F. Emmert-Streib","doi":"10.1109/CANS.2008.17","DOIUrl":null,"url":null,"abstract":"In this paper, we propose an information-theoretic approach to discriminate graph classes structurally. For this, we use a measure for determining the structural information content of graphs. This complexity measure is based on a special information functional that quantifies certain structural information of a graph. To demonstrate that the complexity measure captures structural information meaningfully, we interpret some numerical results.","PeriodicalId":50026,"journal":{"name":"Journal of Systems Science & Complexity","volume":"52 1","pages":"77-84"},"PeriodicalIF":2.6000,"publicationDate":"2008-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Network Classes and Graph Complexity Measures\",\"authors\":\"M. Dehmer, Stephan Borgert, F. Emmert-Streib\",\"doi\":\"10.1109/CANS.2008.17\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we propose an information-theoretic approach to discriminate graph classes structurally. For this, we use a measure for determining the structural information content of graphs. This complexity measure is based on a special information functional that quantifies certain structural information of a graph. To demonstrate that the complexity measure captures structural information meaningfully, we interpret some numerical results.\",\"PeriodicalId\":50026,\"journal\":{\"name\":\"Journal of Systems Science & Complexity\",\"volume\":\"52 1\",\"pages\":\"77-84\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2008-11-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Systems Science & Complexity\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://doi.org/10.1109/CANS.2008.17\",\"RegionNum\":3,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Systems Science & Complexity","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1109/CANS.2008.17","RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
In this paper, we propose an information-theoretic approach to discriminate graph classes structurally. For this, we use a measure for determining the structural information content of graphs. This complexity measure is based on a special information functional that quantifies certain structural information of a graph. To demonstrate that the complexity measure captures structural information meaningfully, we interpret some numerical results.
期刊介绍：
The Journal of Systems Science and Complexity is dedicated to publishing high quality papers on mathematical theories, methodologies, and applications of systems science and complexity science. It encourages fundamental research into complex systems and complexity and fosters cross-disciplinary approaches to elucidate the common mathematical methods that arise in natural, artificial, and social systems. Topics covered are:
complex systems,
systems control,
operations research for complex systems,
economic and financial systems analysis,
statistics and data science,
computer mathematics,
systems security, coding theory and crypto-systems,
other topics related to systems science.
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