异质信息网络中基于网络嵌入的影响力最大化

针对当前大部分影响力最大化算法忽略了异质信息网络包含多种节点类型和多种关系类型，且不同类型节点在原始空间无法直接度量的问题，提出了一种异质信息网络中基于网络嵌入的影响力最大化模型（influence maximization based on network embedding，IMNE），用于选择初始扩散节点实现影响力最大化。该模型不仅可以在对异质信息网络进行编码的同时表征异质信息网络中潜在的信息，还可以捕获不同类型节点间影响力的不确定和复杂性。在3个真实数据集上的实验验证了IMNE算法的有效性。

Influence maximization based on network embedding in heterogeneous information networks

Most current influence maximization algorithms ignore the problem that heterogeneous information networks contain multiple node types and relationship types, and different types of nodes cannot be measured in the original workspace. Accordingly, to solve these issues, this paper proposes a novel model for influence maximization based on network embedding in heterogeneous information networks, which helps to realize influence maximization by choosing initial diffusion nodes. The model can not only manifest the potential information in heterogeneous information networks while encoding it but also capture the uncertainty and complexity of influence among different types of nodes. Experimental results on three real datasets demonstrate the effectiveness of the proposed model.