Community models for malicious content detection, which take into account the context from a social graph alongside the content itself, have shown remarkable performance on benchmark datasets. Yet, misinformation and hate speech continue to propagate on social media networks. This mismatch can be partially attributed to the limitations of current evaluation setups that neglect the rapid evolution of online content and the underlying social graph. In this paper, we propose a novel evaluation setup for model generalisation based on our few-shot subgraph sampling approach. This setup tests for generalisation through few labelled examples in local explorations of a larger graph, emulating more realistic application settings. We show this to be a challenging inductive setup, wherein strong performance on the training graph is not indicative of performance on unseen tasks, domains, or graph structures. Lastly, we show that graph meta-learners trained with our proposed few-shot subgraph sampling outperform standard community models in the inductive setup. We make our code publicly available.

翻译：针对恶意内容检测的社区模型，通过结合社交图上下文与内容本身，在基准数据集上展现了卓越性能。然而，虚假信息和仇恨言论仍在社交媒体网络中持续传播。这种不匹配可部分归因于当前评估设置忽略了在线内容及底层社交图的快速演化。本文提出一种基于小样本子图采样的新颖模型泛化评估方案。该方案通过在大图局部探索中利用少量标注样本测试泛化能力，模拟更真实的应用场景。我们证明这是一个具有挑战性的归纳式设置——模型在训练图上的强性能并不预示其在未见任务、领域或图结构上的表现。最后，实验表明，采用我们提出的小样本子图采样训练的图元学习器在归纳式设置中优于标准社区模型。相关代码已公开。