中文摘要：

实证研究表明，社会个体对于不同主题的信息有着不同的偏好，这对于社会网络中的信息传播过程起着非常重要的作用．影响最大化是社会网络信息传播领域中关于影响结点集挖掘的热点课题．它会从社会网络中寻找最具影响力的结点子集，以这些结点为目标进行影响传播时会获得最大的影响范围．以前关于影响最大化算法研究的大部分工作没有考虑社会个体的信息偏好，这大大降低了结果的准确性．为了提高影响最大化算法的效率和种子集的影响范围，提出一种基于信息偏好的2阶段启发式影响结点挖掘策略LGAUP：第1阶段，基于网络中各结点对于信息主题的偏好程度，得到易感染结点网络；第2阶段，在易感染网络中，基于贪心策略进行影响结点的挖掘．实验中，在数据集douban上实现了L—GAUP，GAUP和CELF算法．实验结果表明，与基准算法GAUP相比，L—GAUP不仅在影响范围指标ISST和Is上有着更好的表现，在效率上也有大幅度的提高．

英文摘要：

The empirical research shows that individuals in real social network have different preference for the information with different themes, which plays an important role in information diffusion in social network. Influence maximization is a fundamental issue to find a subset of influential individuals in a social network such that targeting them initially （e. g. to adopt a new product） will maximize the spread of the influence （further adoptions of the new product）. Most previous work of the influence maximization problem doesn＇t take users＇ preference for information theme into account, which greatly reduces the accuracy of result. To further improve the efficiency and performance of influence maximization algorithm, we propose a two-stage L_GAUP algorithm. In the first stage, based on the node＇s preference for the information theme, we can get a sub-graph. Compared with other nodes in the network, the nodes in sub-graph have higher preference values for the given information theme. Then, in the second stage, based on the greedy strategy, we find the top-k influential nods in the sub-graph. In experiments, we conduct algorithm L_GAUP, GAUP and CELF in a real word dataset douban. As for three metrics runtime, IS and ISST, experimental results show that L_GAUP outperforms the benchmark algorithm GAUP greatly.