{"title":"演化社会互动的自适应时间因果网络模型的建模与动态分析。","authors":"Jan Treur","doi":"10.1186/s40649-017-0039-1","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Network-Oriented Modelling based on adaptive temporal-causal networks provides a unified approach to model and analyse dynamics and adaptivity of various processes, including mental and social interaction processes.</p><p><strong>Methods: </strong>Adaptive temporal-causal network models are based on causal relations by which the states in the network change over time, and these causal relations are adaptive in the sense that they themselves also change over time.</p><p><strong>Results: </strong>It is discussed how modelling and analysis of the dynamics of the behaviour of these adaptive network models can be performed. The approach is illustrated for adaptive network models describing social interaction.</p><p><strong>Conclusions: </strong>In particular, the homophily principle and the 'more becomes more' principles for social interactions are addressed. It is shown how the chosen Network-Oriented Modelling method provides a basis to model and analyse these social phenomena.</p>","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":"4 1","pages":"4"},"PeriodicalIF":0.0000,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40649-017-0039-1","citationCount":"10","resultStr":"{\"title\":\"Modelling and analysis of the dynamics of adaptive temporal-causal network models for evolving social interactions.\",\"authors\":\"Jan Treur\",\"doi\":\"10.1186/s40649-017-0039-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Network-Oriented Modelling based on adaptive temporal-causal networks provides a unified approach to model and analyse dynamics and adaptivity of various processes, including mental and social interaction processes.</p><p><strong>Methods: </strong>Adaptive temporal-causal network models are based on causal relations by which the states in the network change over time, and these causal relations are adaptive in the sense that they themselves also change over time.</p><p><strong>Results: </strong>It is discussed how modelling and analysis of the dynamics of the behaviour of these adaptive network models can be performed. The approach is illustrated for adaptive network models describing social interaction.</p><p><strong>Conclusions: </strong>In particular, the homophily principle and the 'more becomes more' principles for social interactions are addressed. It is shown how the chosen Network-Oriented Modelling method provides a basis to model and analyse these social phenomena.</p>\",\"PeriodicalId\":52145,\"journal\":{\"name\":\"Computational Social Networks\",\"volume\":\"4 1\",\"pages\":\"4\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1186/s40649-017-0039-1\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computational Social Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1186/s40649-017-0039-1\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2017/6/12 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"Mathematics\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Social Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1186/s40649-017-0039-1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2017/6/12 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"Mathematics","Score":null,"Total":0}
Modelling and analysis of the dynamics of adaptive temporal-causal network models for evolving social interactions.
Background: Network-Oriented Modelling based on adaptive temporal-causal networks provides a unified approach to model and analyse dynamics and adaptivity of various processes, including mental and social interaction processes.
Methods: Adaptive temporal-causal network models are based on causal relations by which the states in the network change over time, and these causal relations are adaptive in the sense that they themselves also change over time.
Results: It is discussed how modelling and analysis of the dynamics of the behaviour of these adaptive network models can be performed. The approach is illustrated for adaptive network models describing social interaction.
Conclusions: In particular, the homophily principle and the 'more becomes more' principles for social interactions are addressed. It is shown how the chosen Network-Oriented Modelling method provides a basis to model and analyse these social phenomena.
期刊介绍：
Computational Social Networks showcases refereed papers dealing with all mathematical, computational and applied aspects of social computing. The objective of this journal is to advance and promote the theoretical foundation, mathematical aspects, and applications of social computing. Submissions are welcome which focus on common principles, algorithms and tools that govern network structures/topologies, network functionalities, security and privacy, network behaviors, information diffusions and influence, social recommendation systems which are applicable to all types of social networks and social media. Topics include (but are not limited to) the following: -Social network design and architecture -Mathematical modeling and analysis -Real-world complex networks -Information retrieval in social contexts, political analysts -Network structure analysis -Network dynamics optimization -Complex network robustness and vulnerability -Information diffusion models and analysis -Security and privacy -Searching in complex networks -Efficient algorithms -Network behaviors -Trust and reputation -Social Influence -Social Recommendation -Social media analysis -Big data analysis on online social networks This journal publishes rigorously refereed papers dealing with all mathematical, computational and applied aspects of social computing. The journal also includes reviews of appropriate books as special issues on hot topics.
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