提出OCN方法，提升链接预测中高阶共同邻居的利用效率
OCN: Effectively Utilizing Higher-Order Common Neighbors for Better Link Prediction
- 通过正交化消除不同阶次共同邻居的重复信息
- 在多个基准上平均提升7.7%性能
- 适合需要精准链接预测的研究者使用
共同邻居（CNs）及其高阶变体是当前顶尖链接预测方法中广泛使用的成对特征。然而，现有方法常因不同阶次间存在重复而难以充分挖掘其潜力。我们发现这一局限源于两个关键问题：高阶共同邻居中的冗余与过平滑。为此，我们设计了正交化以消除不同阶次间冗余，并采用归一化缓解过平滑。结合两项技术，提出正交共同邻居（OCN），在主流链接预测基准上平均超越最强基线7.7%。我们提供了详尽的理论分析，并通过消融实验验证了正交化与归一化技术的有效性。代码已公开于：https://github.com/qingpingmo/OCN。
原文摘要 · Abstract (English)
Common Neighbors (CNs) and their higher-order variants are important pairwise features widely used in state-of-the-art link prediction methods. However, existing methods often struggle with the repetition across different orders of CNs and fail to fully leverage their potential. We identify that these limitations stem from two key issues: redundancy and over-smoothing in high-order common neighbors. To address these challenges, we design orthogonalization to eliminate redundancy between different-order CNs and normalization to mitigate over-smoothing. By combining these two techniques, we propose Orthogonal Common Neighbor (OCN), a novel approach that significantly outperforms the strongest baselines by an average of 7.7\% on popular link prediction benchmarks. A thorough theoretical analysis is provided to support our method. Ablation studies also verify the effectiveness of our orthogonalization and normalization techniques. Code is available at: https://github.com/qingpingmo/OCN.
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