Information propagation modeling and influence maximization are two important research problems in viral marketing. When marketing information is given, how can the seed nodes be efficiently identified to maximize the spread of the information through the network? To answer this question, we consider multiple factors in information propagation, such as information content, social influence and user authority, and propose a multifactor-based information propagation model (MFIP). Then, we utilize the first-order influence of the nodes to approximate their influence and propose an efficient heuristic algorithm named weighted degree decrease (WDD) to select the seed nodes under the MFIP model. Experimental evaluations with four real-world social network datasets demonstrate the effectiveness and efficiency of our algorithm.

信息传播建模和影响最大化是病毒营销中的两个重要研究问题。当给出营销信息时，如何有效地识别种子节点以最大化信息在网络中的传播？为了回答这个问题，我们考虑了信息传播中的多个因素，例如信息内容，社会影响力和用户权限，并提出了一种基于多因素的信息传播模型（MFIP）。然后，我们利用节点的一阶影响来近似其影响，并提出了一种有效的启发式算法，称为加权度降低（WDD），以选择MFIP模型下的种子节点。通过对四个真实世界社交网络数据集的实验评估，证明了我们算法的有效性和效率。