提出高维节点嵌入COVE，用随机游走共现建模相似性，降维后性能略升。
Leveraging Non-linear Dimension Reduction and Random Walk Co-occurrence for Node Embedding
- 基于随机游走共现构建高维可解释嵌入
- 降维后聚类与链接预测性能小幅提升
- 适合关注可解释性的图分析任务
通过非线性降维技术，突破节点嵌入的低维限制，提出可解释的高维嵌入方法COVE。该方法在使用UMAP降维至低维时，聚类和链接预测任务性能略有提升。其思想源自神经嵌入中以随机游走共现作为相似性指示，并与扩散过程密切相关。在近期社区检测基准上，采用COVE+UMAP+HDBSCAN的流程表现与流行的Louvain算法相当。
原文摘要 · Abstract (English)
Leveraging non-linear dimension reduction techniques, we remove the low dimension constraint from node embedding and propose COVE, an explainable high dimensional embedding that, when reduced to low dimension with UMAP, slightly increases performance on clustering and link prediction tasks. The embedding is inspired by neural embedding methods that use co-occurrence on a random walk as an indication of similarity, and is closely related to a diffusion process. Extending on recent community detection benchmarks, we find that a COVE UMAP HDBSCAN pipeline performs similarly to the popular Louvain algorithm.
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