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Description: 这篇文章讲述了如何在网络拍卖系统中推荐值得信赖的卖主。该技术是基于社会网络的。-This article describes how the auction system can recommend trusted sellers in the network auctions. This technology is based on social networks.
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Size: 717824 |
Author: 李桃 |
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Description: Social Networks Analysis
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Size: 1398784 |
Author: Cid |
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Description: 对社会网络进行谱不变的随机扰动方法，保护社会网络中个体用户的隐私-randomizing social network：a spectrum preserving approach
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Size: 430080 |
Author: 谢小红 |
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Description: 本文主要介绍社交网络的可视化，从经典的弹簧模型到现在最新算法，可以做为综述性的工作来看。-This paper describes the visualization of social networks, from classic spring model algorithm to the present date, the work can be summarized as the view.
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Size: 391168 |
Author: lovell |
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Description: 利用OpenGL进行社会网络可视化的论文-Thesis of social network visualization with OpenGL
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Size: 3474432 |
Author: yaolong |
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Description: 静态语义的社会网络分析。数据挖掘方面值得一看的论文-Static and Semantic Social Networks Analysis
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Size: 143360 |
Author: 邹润阳 |
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Description: 社交语义网络的书籍，详细介绍了社交语义网络的相关知识，英文版-Social semantic network of books, detailed knowledge of the social semantic network, the English version
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Size: 165888 |
Author: meng |
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Description: 《Mining the Social Web》社交网络中的数据挖掘，对python基础有所需求。-" Mining the Social Web" Data Mining in social networks, the demand for the python foundation.
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Size: 4429824 |
Author: 铭泽 |
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Description: It is a presentation related to privacy in social networks.
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Size: 598016 |
Author: Ferran |
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Description: Introduction to text mining for social networks and named entity recognition
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Size: 1804288 |
Author: icypriest |
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Description: 结合用户和内容矩阵，实现在社交网络上更加优化的内容推荐。-Combination of user and content matrix to achieve more on social networks optimized content recommendation.
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Size: 3341312 |
Author: 苏舟 |
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Description: 基于社会网络的团队构建算法研究，在各个领域应用范围很广-Efficient Bi-objective Team Formation in Social Networks
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Size: 171008 |
Author: 林琳 |
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Description: 基于agent的社交网络建模，通过文本获取，网络分析，找出社交网络中的hub节点-Agent-based modeling of social networks, via text capture, network analysis to identify the social network hub node
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Size: 1999872 |
Author: 张三 |
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Description: 社会网络挖掘 随书配套代码下载，详尽，有效-Social Network Mining Codes matching with the book, detailed and effective
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Size: 753664 |
Author: 李文涛 |
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Description: 该案例为中国人民大学DMC社交网络课程中所用到的电子素材，有ppt及相关文章深入浅出的讲解使的在短时间内了解社交网络的基本概念和应用-The case for the People' s University of China DMC social networks used by the electronic course material, there ppt and related articles explain in simple terms so that the basic concepts and applications in a short time to understand social networks
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Size: 5496832 |
Author: 王蕾 |
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Description: 主要内容为R语言环境下的社交网络数据挖掘，附有源代码和数据，并包含案例所使用的PPT和相关文献。-The main content is under R locales social network data mining, with the source code and data, and includes cases PPT and related documentation used.
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Size: 6829056 |
Author: 王蕾 |
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Description: USER INFLUENTIAL TOPIC SEARCH IN SOCIAL NETWORKS
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Size: 638976 |
Author: robin |
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Description: 网络谣言传播模型等。八卦几乎不可避免地出现在真实的社交网络中。在这篇文章中我们调查
一个人的朋友数量之间的关系和对八卦有多远的限制
该人可以在网络中传播。(Gossip almost inevitably arises in real social networks. In this article we investigate the relationship between the number of friends of a person .)
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Size: 1189888 |
Author: lxy3103
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Description: social networks mining
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Size: 753664 |
Author: H.Shariat
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Description: 针对移动社交网络中节点中心性预测问题，提出基于K阶马尔科夫链的中心性预测方法。在真实移动社交数据集的中计算信息熵分析节点中心性的过去与未来规律性，研究了节点中心性的可预测性。利用节点中心性的历史信息，构建状态转移概率矩阵，预测节点未来中心性值, 并通过分析真实值与预测值之间的误差评估了这些预测方法的性能。结果表明，当阶数K=2时，与四种基于时窗的中心性预测方法比较，基于K阶马尔科夫链的预测模型在MIT数据集和Infocom 06数据集中虽不在个体上优于已提出的预测方法，但在整体上达到了优化。(we proposed a centrality prediction method based on K-order Markov chains to solve the problem of centrality prediction in mobile social networks. In the real mobile social data set, the information entropy of the computation is used to analyze the past and future regularity of the node's centrality, and the predictability of the node's centrality is verified. Using the historical information of the center of the node, the state probability matrix is constructed to predict the future central value of the node. Through the analysis of the error between real value and predicted value, we evaluate the performance of the prediction methods. The results show that the prediction model based on the K-order Markov chain when K=2 is not optimized on the individual on the MIT dataset and the Infocom 06 data set, but on the whole to achieve the optimization.)
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Size: 21229568 |
Author: garyppap |
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