In this paper, we study two tightly coupled topics in online social networks (OSN): relationship classi\ufb01cation and information propagation. The links in a social network often re\ufb02ect social relationships among users. In this work, we \ufb01rst investigate identifying the relationships among social network users based on certain social network property and limited preknown information. Social networks have been widely used for online marketing. A critical step is the propagation maximization by choosing a small set of seeds for marketing. Based on the social relationships learned in the \ufb01rst step, we show how to exploit these relationships to maximize the marketing ef\ufb01cacy. We evaluate our approach on large scale real-world data from Renren network, showing that the performances of our relationship classi\ufb01cation and propagation maximization algorithm are pretty good in practice.<\/p>\n","protected":false},"excerpt":{"rendered":"
In this paper, we study two tightly coupled topics in online social networks (OSN): relationship classi\ufb01cation and information propagation. The links in a social network often re\ufb02ect social relationships among users. In this work, we \ufb01rst investigate identifying the relationships among social network users based on certain social network property and limited preknown information. 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