发现谣言传播树很宽而非很深，提出自适应对比学习方法提升检测效果。
Propagation Tree Is Not Deep: Adaptive Graph Contrastive Learning Approach for Rumor Detection
- 基于节点中心性动态生成多视角，聚焦谣言传播中的关键子结构。
- 在四个数据集上超越现有最佳方法，尤其在浅层回复场景下表现优异。
- 适用于社交网络谣言检测，也可推广至其他树状图结构任务。
社交媒体上的谣言检测日益重要。现有图模型通常假设谣言传播树（RPT）具有深层结构，并沿分支学习序列立场特征。但通过对真实数据集的统计分析，我们发现RPT呈现广泛结构，多数节点仅为1层回复。为聚焦于密集子结构，我们提出谣言自适应图对比学习（RAGCL）方法，通过节点中心性引导的自适应视图增强。总结出三条RPT增强原则：1）排除根节点；2）保留深层回复节点；3）在深层区域保留低层级节点。采用基于中心性重要性得分的节点删除、属性掩码和边删除策略生成视图，再通过图对比目标学习鲁棒的谣言表征。在四个基准数据集上的大量实验表明，RAGCL优于当前最优方法。本工作揭示了RPT的宽结构特性，并提出了针对谣言检测的有原则的自适应增强图对比学习方法。所提原则与技术可潜在应用于其他树状图结构任务。
原文摘要 · Abstract (English)
Rumor detection on social media has become increasingly important. Most existing graph-based models presume rumor propagation trees (RPTs) have deep structures and learn sequential stance features along branches. However, through statistical analysis on real-world datasets, we find RPTs exhibit wide structures, with most nodes being shallow 1-level replies. To focus learning on intensive substructures, we propose Rumor Adaptive Graph Contrastive Learning (RAGCL) method with adaptive view augmentation guided by node centralities. We summarize three principles for RPT augmentation: 1) exempt root nodes, 2) retain deep reply nodes, 3) preserve lower-level nodes in deep sections. We employ node dropping, attribute masking and edge dropping with probabilities from centrality-based importance scores to generate views. A graph contrastive objective then learns robust rumor representations. Extensive experiments on four benchmark datasets demonstrate RAGCL outperforms state-of-the-art methods. Our work reveals the wide-structure nature of RPTs and contributes an effective graph contrastive learning approach tailored for rumor detection through principled adaptive augmentation. The proposed principles and augmentation techniques can potentially benefit other applications involving tree-structured graphs.
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