Abstract: With the development of society, security issues in various types of networks have become increasingly prominent, especially network propagation issues. Accurately locating the diffusion source points of network propagation is an important means to control network propagation. The research on the source location of network propagation also faces problems such as diverse network structure and complex dissemination mechanism. Therefore, this paper studies the problem of source location of network propagation based on graph neural networks, and an invertible graph diffusion model based on graph convolutional networks (GCNIGD) is proposed. In the stage of node susceptibility estimation, the graph convolutional neural network is combined to make full use of the structural information of the network considering the connection relationship between network nodes. In the stage of node feature construction, the graph diffusion theory is combined to spatially localize the information propagation in the network, so that the graph-based model can be enhanced by learning from multi-hop information. In the stage of source localization, the graph traceability problem is transformed into the inverse problem of graph diffusion, a reversible graph network is constructed to accurately estimate the source node, and the ill-posed problems in network traceability are solved. Finally, extensive experiments are conducted on six real-world datasets, and the results show that the proposed method outperforms the state-of-the-art methods. This study has important guiding significance for network security issues such as false information traceability, network attack traceability, etc.

Key words: source localization, graph neural networks, graph diffusion, inverse problem

摘要： 随着社会的发展，各种类型网络的安全问题日益突出，尤其是网络传播问题。对网络传播的扩散源点进行准确的定位是实现控制网络传播的重要手段。对于网络传播溯源问题的研究还面临着网络结构多样、传播机制复杂等问题，因此基于图神经网络研究了网络传播溯源问题，提出了一个基于图卷积神经网络的可逆图扩散模型（GCNIGD）。在节点易感性估计阶段，考虑到网络节点之间的连接关系，结合图卷积神经网络充分利用了网络的结构信息；在节点特征构造阶段，结合了图扩展卷积对网络中信息传递进行空间局部化拓展，从而可以从多跳信息中学习来增强基于图的模型；在进行溯源阶段，将图溯源问题转化为图扩散的逆问题，构造了可逆的图网络对源节点进行准确估计，解决了网络溯源中的不适定问题。最后，在六个真实世界数据集中进行了大量的实验，实验结果表明提出的方法超越了目前已知的最先进方法。该研究对于网络中虚假信息溯源、网络攻击溯源等网络安全领域问题具有重要的指导意义。

关键词: 网络溯源, 图神经网络, 图扩散, 逆问题