Nodes in the real-world graphs exhibit diverse patterns in numerous aspects, such as degree and homophily. However, most existent node predictors fail to capture a wide range of node patterns or to make predictions based on distinct node patterns, resulting in unsatisfactory classification performance. In this paper, we reveal that different node predictors are good at handling nodes with specific patterns and only apply one node predictor uniformly could lead to suboptimal result. To mitigate this gap, we propose a mixture of experts framework, MoE-NP, for node classification. Specifically, MoE-NP combines a mixture of node predictors and strategically selects models based on node patterns. Experimental results from a range of real-world datasets demonstrate significant performance improvements from MoE-NP.

翻译：现实世界图中的节点在诸多方面（如度与同质性）呈现出多样化的模式。然而，现有的大多数节点预测器未能捕获广泛的节点模式，或未能基于不同的节点模式进行预测，导致分类性能不尽如人意。本文揭示，不同的节点预测器擅长处理具有特定模式的节点，而统一应用单一节点预测器会导致次优结果。为弥补这一不足，我们提出了一种用于节点分类的专家混合框架——MoE-NP。具体而言，MoE-NP结合了多种节点预测器，并依据节点模式策略性地选择模型。在多个真实世界数据集上的实验结果表明，MoE-NP带来了显著的性能提升。