Zero-shot graph anomaly detection (GAD) has attracted increasing attention recent years, yet the heterogeneity of graph structures, features, and anomaly patterns across graphs make existing single GNN methods insufficiently expressive to model diverse anomaly mechanisms. In this regard, Mixture-of-experts (MoE) architectures provide a promising paradigm by integrating diverse GNN experts with complementary inductive biases, yet their effectiveness in zero-shot GAD is severely constrained by distribution shifts, leading to two key routing challenges. First, nodes often carry vastly different semantics across graphs, and straightforwardly performing routing based on their features is prone to generating biased or suboptimal expert assignments. Second, as anomalous graphs often exhibit pronounced distributional discrepancies, existing router designs fall short in capturing domain-invariant routing principles that generalize beyond the training graphs. To address these challenges, we propose a novel MoE framework with evolutionary router feature generation (EvoFG) for zero-shot GAD. To enhance MoE routing, we propose an evolutionary feature generation scheme that iteratively constructs and selects informative structural features via an LLM-based generator and Shapley-guided evaluation. Moreover, a memory-enhanced router with an invariant learning objective is designed to capture transferable routing patterns under distribution shifts. Extensive experiments on six benchmarks show that EvoFG consistently outperforms state-of-the-art baselines, achieving strong and stable zero-shot GAD performance.

翻译：零样本图异常检测近年来受到越来越多的关注，然而图结构、特征及异常模式在不同图之间的异质性，使得现有的单一图神经网络方法不足以建模多样化的异常机制。在这方面，专家混合架构通过整合具有互补归纳偏置的多样化图神经网络专家，提供了一个有前景的范式，但其在零样本图异常检测中的有效性受到分布偏移的严重制约，导致两个关键的路由挑战。首先，节点在不同图中通常承载着截然不同的语义，直接基于节点特征进行路由容易产生有偏或次优的专家分配。其次，由于异常图常表现出显著的分布差异，现有的路由器设计难以捕获超越训练图泛化的领域不变路由原则。为应对这些挑战，我们提出了一种新颖的专家混合框架，其配备进化路由特征生成方法，用于零样本图异常检测。为增强专家混合路由，我们提出了一种进化特征生成方案，该方案通过基于大语言模型的生成器和基于沙普利值的评估，迭代地构建并选择信息丰富的结构特征。此外，设计了一个具有不变学习目标的记忆增强路由器，以捕获分布偏移下可迁移的路由模式。在六个基准数据集上的大量实验表明，所提方法持续优于最先进的基线模型，实现了强大且稳定的零样本图异常检测性能。