异质信息网络中基于有向无环图的影响力最大化算法

Influence maximization algorithm based on directed acyclic graph in heterogeneous information networks

针对异质信息网络中的影响力最大化(IM)问题,提出了一种基于有向无环图(DAG)的影响力最大化算法(DAGIM)。首先基于DAG结构度量节点的影响力,然后采用边际增益策略选择影响力最大的节点。DAG结构表达力强,不仅描述了不同类型节点之间的显性关系,也刻画了节点之间的隐性关系,较完整地保留了网络的异质信息。在三个真实数据集上的实验结果验证所提DAGIM的性能优于Degree、PageRank、局部有向无环图(LDAG)以及基于元路径的信息熵(MPIE)算法。

Aiming at the problem of Influence Maximization(IM)in heterogeneous information networks,an Influence Maximization algorithm(DAGIM)based on Directed Acyclic Graph(DAG)was proposed.Firstly,the influence of nodes was measured based on the DAG structure,and then the marginal gain strategy was used to select the nodes with the most influence.The DAG structure has strong expressive power,which not only describes the explicit relationship between different types of nodes,but also depicts the implicit relationship between nodes,and more completely retains the heterogeneous information of the network.Experimental results on three real datasets verify that the performance of the proposed DAGIM algorithm is better than those of Degree,PageRank,Local Directed Acyclic Graph(LDAG)and MetaPath-based Information Entropy(MPIE)algorithms.