Titlebook: Computational Social Networks; 4th International Co My T. Thai,Nam P. Nguyen,Huawei Shen Conference proceedings 2015 Springer International

Monomania

书目名称Computational Social Networks影响因子(影响力)




书目名称Computational Social Networks影响因子(影响力)学科排名




书目名称Computational Social Networks网络公开度




书目名称Computational Social Networks网络公开度学科排名




书目名称Computational Social Networks被引频次




书目名称Computational Social Networks被引频次学科排名




书目名称Computational Social Networks年度引用




书目名称Computational Social Networks年度引用学科排名




书目名称Computational Social Networks读者反馈




书目名称Computational Social Networks读者反馈学科排名

大酒杯

Pricing in Social Networks with Negative Externalities,f the seller. We assume impatient consumers who buy the product as soon as the seller posts a price not greater than their valuations of the product. The product’s value for a consumer is determined by two factors: a fixed consumer-specified intrinsic value and a variable externality that is exerted

娴熟

Preventing and Detecting Infiltration on Online Social Networks,nt purposes, such as: communicating, information sharing, relations creating or for business purposes, and etc. However, accompanying with benefits of social networks, users also must face the security and privacy risks. These issues have recently paid much attention to. One of these issues is the u

笨重

裹住

Substance

Substance

Clustering 1-Dimensional Periodic Network Using Betweenness Centrality,raphs. While the temporal networks have a wide range of applications such as opportunistic communication, there are not many clustering algorithms specifically proposed for them. We give a pseudo polynomial-time algorithm for temporal networks, of which the transit value is always positive and the l

丛林

seruting

Saline

A Point-of-Interest Recommendation Method Based on User Check-in Behaviors in Online Social Network visiting them. The paper takes the scalability of information and user personalization into consideration to improve POI recommendation service, and proposes a personalized POI recommendation method based on user check-in behaviors in online social networks. First, the user’s travel experience in t