Key Laboratory of Intelligent Information Processing and Graph Processing, Guangxi University of Science and Technology, Liuzhou, Guangxi, China.
School of Computer Science and Technology, Guangxi University of Science and Technology, Liuzhou, Guangxi, China.
PLoS One. 2023 May 15;18(5):e0285563. doi: 10.1371/journal.pone.0285563. eCollection 2023.
The diffusion phenomena taking place in complex networks are usually modelled as diffusion process, such as the diffusion of diseases, rumors and viruses. Identification of diffusion source is crucial for developing strategies to control these harmful diffusion processes. At present, accurately identifying the diffusion source is still an opening challenge. In this paper, we define a kind of diffusion characteristics that is composed of the diffusion direction and time information of observers, and propose a neural networks based diffusion characteristics classification framework (NN-DCCF) to identify the source. The NN-DCCF contains three stages. First, the diffusion characteristics are utilized to construct network snapshot feature. Then, a graph LSTM auto-encoder is proposed to convert the network snapshot feature into low-dimension representation vectors. Further, a source classification neural network is proposed to identify the diffusion source by classifying the representation vectors. With NN-DCCF, the identification of diffusion source is converted into a classification problem. Experiments are performed on a series of synthetic and real networks. The results show that the NN-DCCF is feasible and effective in accurately identifying the diffusion source.
在复杂网络中发生的扩散现象通常被建模为扩散过程，例如疾病、谣言和病毒的扩散。识别扩散源对于制定控制这些有害扩散过程的策略至关重要。目前，准确识别扩散源仍然是一个开放的挑战。在本文中，我们定义了一种由观察者的扩散方向和时间信息组成的扩散特征，并提出了一种基于神经网络的扩散特征分类框架（NN-DCCF）来识别源。NN-DCCF 包含三个阶段。首先，利用扩散特征构建网络快照特征。然后，提出了一个图 LSTM 自动编码器将网络快照特征转换为低维表示向量。进一步，提出了一个源分类神经网络通过分类表示向量来识别扩散源。通过 NN-DCCF，将扩散源的识别转化为分类问题。在一系列合成和真实网络上进行了实验。结果表明，NN-DCCF 能够在准确识别扩散源方面是可行和有效的。
