提出新型图神经网络聚合方法，提升邻居特征混合能力
Sequential Signal Mixing Aggregation for Message Passing Graph Neural Networks
- 将邻居特征视为二维离散信号，分步卷积实现特征混合
- 在多个基准上取得显著性能提升，部分达到新最好结果
- 可直接替换现有聚合模块，适合图学习研究者使用
消息传递图神经网络（MPGNNs）已成为建模复杂图结构实体间交互的首选方法。尽管其理论基础清晰，但聚合模块尚未得到足够重视。基于求和的聚合器虽有坚实的分离能力理论支持，但实践中人们更倾向使用复杂聚合或多种聚合的混合。本文揭示了这一差距的原因：求和聚合无法有效混合来自不同邻居的特征，从而限制了下游任务表现。为此，我们提出序列信号混合聚合（SSMA），一种新型即插即用的聚合方法。SSMA将邻居特征视为二维离散信号，通过序列卷积操作，天然增强了对不同邻居特征的混合能力。大量实验表明，将SSMA与主流MPGNN架构结合后，在多个基准测试中均取得显著性能提升，多项任务达到新的最佳水平。代码已公开于https://almogdavid.github.io/SSMA/
原文摘要 · Abstract (English)
Message Passing Graph Neural Networks (MPGNNs) have emerged as the preferred method for modeling complex interactions across diverse graph entities. While the theory of such models is well understood, their aggregation module has not received sufficient attention. Sum-based aggregators have solid theoretical foundations regarding their separation capabilities. However, practitioners often prefer using more complex aggregations and mixtures of diverse aggregations. In this work, we unveil a possible explanation for this gap. We claim that sum-based aggregators fail to "mix" features belonging to distinct neighbors, preventing them from succeeding at downstream tasks. To this end, we introduce Sequential Signal Mixing Aggregation (SSMA), a novel plug-and-play aggregation for MPGNNs. SSMA treats the neighbor features as 2D discrete signals and sequentially convolves them, inherently enhancing the ability to mix features attributed to distinct neighbors. By performing extensive experiments, we show that when combining SSMA with well-established MPGNN architectures, we achieve substantial performance gains across various benchmarks, achieving new state-of-the-art results in many settings. We published our code at \url{https://almogdavid.github.io/SSMA/}
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