提出一种稳定动态网络嵌入方法，无需标签即可保证行为相似节点在不同时刻获得相同表示。
Unsupervised Attributed Dynamic Network Embedding with Stability Guarantees
- 基于谱嵌入思想，通过时间展开的邻接矩阵建模动态网络结构与属性变化。
- 在四个真实数据集上验证，相比现有方法显著提升链接预测与节点分类性能。
- 首个无需标签即具备稳定性保证的属性化动态网络嵌入方法，适合时序图分析任务。
动态网络嵌入的稳定性确保行为相似的节点在不同时刻获得相同的表示，从而支持跨时间的节点比较。本文提出属性化展开邻接谱嵌入（AUASE），一种针对带有时变协变量信息的动态网络的无监督表示学习框架。为建立稳定性，我们证明了其一致收敛到对应的潜在位置模型。通过在四个真实属性网络上与最先进的网络表示学习方法对比，量化了所提动态嵌入的优势。据我们所知，AUASE是唯一无需真实标签即可满足稳定性保证的属性化动态嵌入方法，实验表明其在链接预测和节点分类任务上均取得显著提升。
原文摘要 · Abstract (English)
Stability for dynamic network embeddings ensures that nodes behaving the same at different times receive the same embedding, allowing comparison of nodes in the network across time. We present attributed unfolded adjacency spectral embedding (AUASE), a stable unsupervised representation learning framework for dynamic networks in which nodes are attributed with time-varying covariate information. To establish stability, we prove uniform convergence to an associated latent position model. We quantify the benefits of our dynamic embedding by comparing with state-of-the-art network representation learning methods on four real attributed networks. To the best of our knowledge, AUASE is the only attributed dynamic embedding that satisfies stability guarantees without the need for ground truth labels, which we demonstrate provides significant improvements for link prediction and node classification.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。