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Titlebook: Data Mining for Social Network Data; Nasrullah Memon,Jennifer Jie Xu,Hsinchun Chen Book 2010 Springer Science+Business Media, LLC 2010 Map

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書目名稱Data Mining for Social Network Data
編輯Nasrullah Memon,Jennifer Jie Xu,Hsinchun Chen
視頻videohttp://file.papertrans.cn/263/262953/262953.mp4
概述Using machine learning techniques to analyze social networks.A multidisciplinary source, which will draw the interest of researchers and students in sociology, computer science and statistics.The rese
叢書名稱Annals of Information Systems
圖書封面Titlebook: Data Mining for Social Network Data;  Nasrullah Memon,Jennifer Jie Xu,Hsinchun Chen Book 2010 Springer Science+Business Media, LLC 2010 Map
描述Driven by counter-terrorism efforts, marketing analysis and an explosion in online social networking in recent years, data mining has moved to the forefront of information science. This proposed Special Issue on Data Mining for Social Network Data will present a broad range of recent studies in social networking analysis. It will focus on emerging trends and needs in discovery and analysis of communities, solitary and social activities, activities in open for a and commercial sites as well. It will also look at network modeling, infrastructure construction, dynamic growth and evolution pattern discovery using machine learning approaches and multi-agent based simulations.Editors are three rising stars in world of data mining, knowledge discovery, social network analysis, and information infrastructures, and are anchored by Springer author/editor Hsinchun Chen (Terrorism Informatics; Medical Informatics; Digital Government), who is one of the most prominent intelligence analysis and data mining experts in the world.
出版日期Book 2010
關(guān)鍵詞Mapping; Simulation; algorithms; calculus; data mining; information science; learning; machine learning; mod
版次1
doihttps://doi.org/10.1007/978-1-4419-6287-4
isbn_softcover978-1-4419-6286-7
isbn_ebook978-1-4419-6287-4Series ISSN 1934-3221 Series E-ISSN 1934-3213
issn_series 1934-3221
copyrightSpringer Science+Business Media, LLC 2010
The information of publication is updating

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Modularity for Bipartite Networks,orks. Discovering communities from such bipartite networks is important for finding similar items and for understanding overall network structures. In order to evaluate the quality of divisions of normal (unipartite) networks, Newman’s modularity is widely used. Recently, modularities for bipartite
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Framework for Fast Identification of Community Structures in Large-Scale Social Networks,other when compared to the rest of the networks, which encode the information about the organization and functionality of the nodes. Social networking sites (SNS), which allow the interaction of millions of users, have important scientific and practical implications; however, they require the develo
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Integrating Genetic Algorithms and Fuzzy Logic for Web Structure Optimization,ges of the considered Website. Fuzzy logic gives a degree of a membership to a problem and, hence, more adequately describes reasoning to a problem than a numeric deviation value does (the difference between the WPR index and log rank index), which does not give accurate human reasoning. Using fuzzy
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