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Titlebook: Social Network Data Analytics; Charu C. Aggarwal Book 2011 Springer Science+Business Media, LLC 2011 Fuzzy social networks.Intelligent soc

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樓主: 掩飾
21#
發(fā)表于 2025-3-25 06:06:22 | 只看該作者
A Survey of Link Prediction in Social Networks,tics and e-commerce. There exist a variety of techniques for link prediction, ranging from feature-based classification and kernel-based method to matrix factorization and probabilistic graphical models. These methods differ from each other with respect to model complexity, prediction performance, s
22#
發(fā)表于 2025-3-25 11:32:49 | 只看該作者
23#
發(fā)表于 2025-3-25 14:47:15 | 只看該作者
Visualizing Social Networks,es critically important. However, as networks become larger and more complex, reasoning about social dynamics via simple statistics is not a feasible option. To overcome these limitations, we can rely on visual metaphors. Visualization nowadays is no longer a passive process that produces images fro
24#
發(fā)表于 2025-3-25 19:42:38 | 只看該作者
Data Mining in Social Media,eeded to analyze large, complex, and frequently changing social media data. This chapter introduces the basics of data mining, reviews social media, discusses how to mine social media data, and highlights some illustrative examples with an emphasis on social networking sites and blogs.
25#
發(fā)表于 2025-3-25 23:50:43 | 只看該作者
26#
發(fā)表于 2025-3-26 03:47:35 | 只看該作者
27#
發(fā)表于 2025-3-26 08:09:02 | 只看該作者
28#
發(fā)表于 2025-3-26 10:26:08 | 只看該作者
An Overview of Social Tagging and Applications,rease in the kinds of web objects becoming available, collaborative tagging of such objects is also developing along new dimensions. This popularity has led to a vast literature on social tagging. In this survey paper, we would like to summarize different techniques employed to study various aspects
29#
發(fā)表于 2025-3-26 14:57:41 | 只看該作者
30#
發(fā)表于 2025-3-26 16:50:09 | 只看該作者
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