发表机构
Griffith University（格里菲斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对异质性图中开放集节点分类的挑战，提出HOPE方法，通过结构增强初始化、可信邻域聚合和异质性引导的伪外推策略，有效提升未知类拒绝能力，并在多个数据集上优于现有模型。
AI 中文摘要
标准的开放集节点分类方法依赖于同质性假设，即连接的节点共享标签。然而，现实世界的图通常是异质性的，这暴露了当前方法的局限性，并对开放集节点分类提出了新的挑战。一方面，跨类连接导致不同已知或未知类别的表示在聚合后相互交织，削弱了它们的判别能力。另一方面，结构混合使得基于阈值的开放集方法和跨类特征插值失效，导致不可靠的未知类拒绝。为了解决这些挑战，我们提出了HOPE，一种具有伪外推的异质性感知开放集节点分类方法。为了使开放集图神经网络（GNN）适应异质性场景，HOPE使用结构增强的特征初始化层来捕获多跳结构模式。同时，我们为标准GNN设计了一种可信的邻域聚合机制，以动态过滤噪声的跨类邻居。为了增强未知类拒绝，我们引入了一种异质性引导的伪外推策略。它动态维护已知类中心，并沿跨类邻域位移方向进行外推，在结构模糊区域附近合成伪未知代理。最后，我们通过联合分类和logit边缘正则化来优化网络，将合成代理路由到专用的拒绝槽中，而无需在表示空间中施加几何边缘约束。在多个数据集上的大量实验表明，HOPE始终优于最先进的模型，验证了其有效性、鲁棒性和效率。
英文摘要
Standard open-set node classification methods rely on the homophily assumption, where connected nodes share labels. However, real-world graphs are often heterophilic, exposing the limitations of current methods and posing new challenges to open-set node classification. On the one hand, cross-class connectivity causes representations from different known or unknown classes to become intertwined after aggregation, undermining their discriminative capacity. On the other hand, structural mixture invalidates threshold-based open-set methods and cross-class feature interpolation, leading to unreliable unknown-class rejection. To address these challenges, we propose HOPE, a Heterophily-aware Open-set node classification method with Pseudo-Extrapolation. To adapt open-set graph neural networks (GNNs) to heterophilic scenarios, HOPE uses a structure-augmented feature initialization layer to capture multi-hop structural patterns. Meanwhile, we design a trustworthy neighborhood aggregation mechanism for standard GNNs to dynamically filter noisy cross-class neighbors. To enhance unknown-class rejection, we introduce a heterophily-guided pseudo-extrapolation strategy. It dynamically maintains known-class centers and extrapolates along cross-class neighborhood displacement directions, synthesizing pseudo-unknown proxies near structurally ambiguous regions. Finally, we optimize the network with joint classification and logit margin regularization, routing synthetic proxies into a dedicated rejection slot without imposing geometric margin constraints in the representation space. Extensive experiments on multiple datasets show that HOPE consistently outperforms state-of-the-art models, validating its effectiveness, robustness, and efficiency.
