让节点嵌入自动解释自己，每个维度对应图结构的特定部分。
Disentangled and Self-Explainable Node Representation Learning
- 通过解耦学习，让嵌入的每个维度对应图的不同拓扑结构。
- 在多个基准数据集上同时提升可解释性和表示性能。
- 适合关注模型透明度与图神经网络可解释性的研究者。
节点嵌入是捕捉节点属性的低维向量，通常通过无监督结构相似性目标或有监督任务学习。尽管近期研究聚焦于解释图模型决策，但无监督节点嵌入的可解释性仍缺乏探索。为此，我们提出DiSeNE（解耦且自解释节点嵌入）框架，在无监督条件下生成可自解释的嵌入。该方法采用解耦表示学习，使嵌入的每个维度对齐图的不同拓扑结构。我们提出了新的期望准则，驱动新型目标函数，同时优化可解释性与解耦性。此外，我们设计了多项新指标评估表示质量与人类可读性。在多个基准数据集上的实验验证了该方法的有效性。
原文摘要 · Abstract (English)
Node representations, or embeddings, are low-dimensional vectors that capture node properties, typically learned through unsupervised structural similarity objectives or supervised tasks. While recent efforts have focused on explaining graph model decisions, the interpretability of unsupervised node embeddings remains underexplored. To bridge this gap, we introduce DiSeNE (Disentangled and Self-Explainable Node Embedding), a framework that generates self-explainable embeddings in an unsupervised manner. Our method employs disentangled representation learning to produce dimension-wise interpretable embeddings, where each dimension is aligned with distinct topological structure of the graph. We formalize novel desiderata for disentangled and interpretable embeddings, which drive our new objective functions, optimizing simultaneously for both interpretability and disentanglement. Additionally, we propose several new metrics to evaluate representation quality and human interpretability. Extensive experiments across multiple benchmark datasets demonstrate the effectiveness of our approach.
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