Graph Neural Networks (GNNs) have proven to be highly effective for node
classification tasks across diverse graph structural patterns. Traditionally,
GNNs employ a uniform global filter, typically a low-pass filter for homophilic
graphs and a high-pass filter for heterophilic graphs. However, real-world
graphs often exhibit a complex mix of homophilic and heterophilic patterns,
rendering a single global filter approach suboptimal. In this work, we
theoretically demonstrate that a global filter optimized for one pattern can
adversely affect performance on nodes with differing patterns. To address this,
we introduce a novel GNN framework Node-MoE that utilizes a mixture of experts
to adaptively select the appropriate filters for different nodes. Extensive
experiments demonstrate the effectiveness of Node-MoE on both homophilic and
heterophilic graphs.

图神经网络 (GNNs) 在各种图结构模式下的节点分类任务中已被证明非常有效。然而，现实世界的图通常展现出复杂的同构和异构模式的混合，使用统一的全局过滤器方法则不够优化。为解决这个问题，我们引入了一种新颖的 GNN 框架 Node-MoE，使用专家混合来自适应地选择不同节点的适当过滤器。广泛的实验证明了 Node-MoE 在同构和异构图上的有效性。