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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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楼主: 掩饰
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Text Mining in Social Networks,s out that the use of a combination of linkage and content information provides much more effective results than a system which is based purely on either of the two. This paper provides a survey of such algorithms, and the advantages observed by using such algorithms in different scenarios. We also present avenues for future research in this area.
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Multimedia Information Networks in Social Media,of recent systems related to multimedia information networks, summarizes the popular inference methods used in recent works, and discusses the applications related to multimedia information networks. We also discuss a wide range of topics including public datasets, related industrial systems, and potential future research directions in this field.
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Statistical Properties of Social Networks,, static properties, or properties describing the structure of snapshots of graphs; and dynamic properties, properties describing how the structure evolves over time. These properties may be for unweighted or weighted graphs, where weights may represent multi-edges (e.g. multiple phone calls from on
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Node Classification in Social Networks,indicate demographic values, interest, beliefs or other characteristics of the nodes (users). A core problem is to use this information to extend the labeling so that all nodes are assigned a label (or labels)..In this chapter, we survey classification techniques that have been proposed for this pro
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A Survey of Algorithms and Systems for Expert Location in Social Networks,call this problem the .problem and we survey its different aspects as they arise in practice. For example, given the activities of candidates within a context (., authoring a document, answering a question), we first describe methods for evaluating the level of expertise for each of them. Often, exp
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