最大化图表示学习中的内聚和分离：一种距离感知的负采样方法
Maximizing Cohesion and Separation in Graph Representation Learning: A Distance-aware Negative Sampling Approach
摘要：
无监督图表示学习（GRL）的目标是学习一个低维的节点嵌入空间，以反映给定的未标记图的结构。用于此任务的现有算法依赖于负采样目标，该负采样目标通过维持节点对的正语料库和负语料库来最大化在附近节点处的节点嵌入中的相似性（被称为“内聚”）。虽然正样本是从短随机游走中共同出现的节点对中提取的，但是传统方法通过均匀地对随机对进行采样来构建负语料库，从而忽略了关于远距离节点对之间的结构不相似性（称为“分离”）的有价值的信息。在本文中，我们提出了一种新的距离感知负采样（DNS），最大限度地提高分离的遥远的节点对，同时最大限度地提高凝聚力在附近的节点对通过设置负采样概率成正比的成对最短距离。我们的方法可以与任何GRL算法结合使用，我们证明了我们的方法的有效性，在一些基准数据集和GRL算法的下游节点分类任务的基线负采样方法。我们所有的代码和数据集都可以在https://github.com/Distance-awareNS/DNS/上找到。
英文摘要：
The objective of unsupervised graph representation learning (GRL) is to learn a low-dimensional space of node embeddings that reflect the structure of a given unlabeled graph. Existing algorithms for this task rely on negative sampling objectives that maximize the similarity in node embeddings at nearby nodes (referred to as "cohesion") by maintaining positive and negative corpus of node pairs. While positive samples are drawn from node pairs that co-occur in short random walks, conventional approaches construct negative corpus by uniformly sampling random pairs, thus ignoring valuable information about structural dissimilarity among distant node pairs (referred to as "separation"). In this paper, we present a novel Distance-aware Negative Sampling (DNS) which maximizes the separation of distant node-pairs while maximizing cohesion at nearby node-pairs by setting the negative sampling probability proportional to the pair-wise shortest distances. Our approach can be used in conjunction with any GRL algorithm and we demonstrate the efficacy of our approach over baseline negative sampling methods over downstream node classification tasks on a number of benchmark datasets and GRL algorithms. All our codes and datasets are available at https://github.com/Distance-awareNS/DNS/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
