Wavefront-Based Multiple Rumor Sources Identification by Multi-Task Learning
Wavefront-Based Multiple Rumor Sources Identification by Multi-Task Learning
复制标题
基于波前的多任务学习识别多谣言源
DOI：
10.1109/tetci.2022.3142627
复制
发表时间：
2022-01-27
影响因子：
5.3
通讯作者：
Zhou, Xiaofang
中科院分区：
文献类型：
--
作者：
Dong, Ming;Zheng, Bolong;Zhou, Xiaofang
在社交网络中识别谣言来源是自动战胜谣言的关键任务之一。许多努力致力于在假设每个节点的感染状态事先已知的情况下定位谣言来源，而其他努力则侧重于识别具有部分感染知识的来源，如波前、稀疏观察者和快照。波前是社会网络中最新传播时被感染的一组节点，最初是为了分析SARS疫情而定义的，在信息源定位任务中具有相当重要的意义。然而，利用波前技术解决多谣传源检测问题的研究很少。在本文中，我们提出了一个序列到序列的模型，称为基于图约束的序列源识别（GCSSI），它以波前作为输入来解决MRSD问题。GCSSI通过采用编码器-解码器结构和基于图约束的多任务学习，对每个时间步的反向谣言传播进行估计，并端到端预测谣言来源。我们在多个真实数据集上进行了实验，实验结果表明我们的模型与已有的工作相比具有优越性。
Identifying rumor sources in social networks is one of the key tasks for defeating rumors automatically. Many efforts have been devoted to locating rumor sources with an assumption that the infected status of each node is known in advance, while other efforts focus on identifying sources with partial infection knowledge, such as wavefront, sparse observers, and snapshots. Wavefront is a set of nodes that are infected at the latest propagation in social networks, which is originally defined for analyzing the SARS epidemic, and shows considerable importance in information source locating task. However, only a few studies are proposed to solve the multiple rumor source detection (MRSD) problem by using wavefront. In this paper, we propose a sequence-to-sequence model, called Graph Constraint based Sequential Source Identification (GCSSI), which takes wavefront as input to solve the MRSD problem. By adopting encoder-decoder structure and graph constraint based multi-task learning, GCSSI estimates the reverse rumor dissemination at each time step and predicts sources in an end-to-end way. We conduct experiments on several real datasets and the experimental results show the superiority of our model compared with existing work.