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

Monomania

書目名稱Computational Social Networks影響因子(影響力)




書目名稱Computational Social Networks影響因子(影響力)學(xué)科排名




書目名稱Computational Social Networks網(wǎng)絡(luò)公開度




書目名稱Computational Social Networks網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Computational Social Networks被引頻次




書目名稱Computational Social Networks被引頻次學(xué)科排名




書目名稱Computational Social Networks年度引用




書目名稱Computational Social Networks年度引用學(xué)科排名




書目名稱Computational Social Networks讀者反饋




書目名稱Computational Social Networks讀者反饋學(xué)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

嫻熟

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

叢林

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