Molaei Soheila, Babaei Sama, Salehi Mostafa, Jalili Mahdi
Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran.
School of Computer Science, Institute for Research in Fundamental Science (IPM), Tehran, Iran.
Sci Rep. 2018 Jun 22;8(1):9549. doi: 10.1038/s41598-018-27385-2.
Diffusion of information in complex networks largely depends on the network structure. Recent studies have mainly addressed information diffusion in homogeneous networks where there is only a single type of nodes and edges. However, some real-world networks consist of heterogeneous types of nodes and edges. In this manuscript, we model information diffusion in heterogeneous information networks, and use interactions of different meta-paths to predict the diffusion process. A meta-path is a path between nodes across different layers of a heterogeneous network. As its most important feature the proposed method is capable of determining the influence of all meta-paths on the diffusion process. A conditional probability is used assuming interdependent relations between the nodes to calculate the activation probability of each node. As independent cascade models, we consider linear threshold and independent cascade models. Applying the proposed method on two real heterogeneous networks reveals its effectiveness and superior performance over state-of-the-art methods.
复杂网络中的信息扩散很大程度上取决于网络结构。最近的研究主要关注同构网络中的信息扩散，其中只有单一类型的节点和边。然而，一些现实世界的网络由不同类型的节点和边组成。在本论文中，我们对异构信息网络中的信息扩散进行建模，并使用不同元路径的相互作用来预测扩散过程。元路径是异构网络不同层中节点之间的路径。作为其最重要的特征，所提出的方法能够确定所有元路径对扩散过程的影响。假设节点之间存在相互依赖关系，使用条件概率来计算每个节点的激活概率。作为独立级联模型，我们考虑线性阈值模型和独立级联模型。将所提出的方法应用于两个真实的异构网络，揭示了其相对于现有方法的有效性和优越性能。
