Lin Yao, Xue Wenhui, Bai Congrui, Li Jing, Yin Xiaoyan, Wu Chase Q
School of Information Science and Technology, Northwest University, Xi'an, China.
Department of Data Science, New Jersey Institute of Technology College of Computing Sciences, Newark, NJ, USA.
Sci Prog. 2025 Jan-Mar;108(1):368504241307816. doi: 10.1177/00368504241307816.
Because of their proficiency in capturing the category characteristics of graphs, graph neural networks have shown remarkable advantages for graph-level classification tasks, that is, rumor detection and anomaly detection. Due to the manipulation of special means (e.g. bots) on online media, rumors may spread across the whole network at an overwhelming speed. Compared with normal information, popular rumors usually have a special propagation structure, especially in the early stage of information dissemination. More specifically, the special propagation structure determines whether rumors can be spread successfully. Namely, online users and their interaction that constitute the special propagation structure play a key role in the spread of rumors. Therefore, the problem of rumor detection can be transformed into detecting the existence of a special propagation structure. Inspired by backdoor attacks, we propose an interpretable rumor detection algorithm based on backdoor. Firstly, based on causal analysis, the causal sub-graph that determines the category of the graph (rumor vs. normal information) is obtained, that is, the critical online users in the rumor spreading effect are found, and then the specific propagation structure is explored. Finally, the special propagation structure is planted into the rumor detection model as a trigger. Experimental results and performance analysis on three real-world datasets demonstrate the effectiveness of our proposed algorithm in the special propagation structure detection of rumors. Compared with two baselines, achieves up to 33.1% and 61.8% performance improvement in terms of attack success rate and clean accuracy drop.
由于图神经网络在捕捉图的类别特征方面表现出色，因此在图级分类任务（即谣言检测和异常检测）中显示出显著优势。由于在线媒体上存在特殊手段（如机器人程序）的操纵，谣言可能会以压倒性的速度在整个网络中传播。与正常信息相比，流行谣言通常具有特殊的传播结构，尤其是在信息传播的早期阶段。更具体地说，这种特殊的传播结构决定了谣言能否成功传播。也就是说，构成特殊传播结构的网络用户及其互动在谣言传播中起着关键作用。因此，谣言检测问题可以转化为检测特殊传播结构的存在。受后门攻击的启发，我们提出了一种基于后门的可解释谣言检测算法。首先，基于因果分析，获得决定图类别（谣言与正常信息）的因果子图，即找出在谣言传播效果中起关键作用的网络用户，然后探索具体的传播结构。最后，将这种特殊传播结构植入谣言检测模型作为触发器。在三个真实世界数据集上的实验结果和性能分析证明了我们提出的算法在谣言特殊传播结构检测中的有效性。与两个基线相比，在攻击成功率和干净准确率下降方面分别实现了高达33.1%和61.8%的性能提升。
