让原本只能处理已知节点的图嵌入方法，也能用于新节点
iN2V: Bringing Transductive Node Embeddings to Inductive Graphs
- 在原始node2vec基础上改进训练和后处理流程，实现对新节点的嵌入
- 在多个数据集上使节点分类准确率平均提升1个百分点，最多提升6个点
- 可作为插件使用，兼容其他嵌入方法，适合需要动态图推理的场景
像node2vec这样的浅层节点嵌入方法可用于无特征节点或以结构信息补充已有特征。但这类方法受限于仅适用于训练时已知的节点，因此仅能用于转换设置（transductive）。本文提出归纳式node2vec（iN2V），通过改进原始node2vec的训练过程，并引入后处理步骤，实现对训练中未见节点的嵌入计算。我们在多个基准数据集上进行了实验，结果表明iN2V能有效将原本仅限于转换设置的嵌入方法拓展到归纳设置。使用iN2V嵌入后，节点分类准确率平均提升1个百分点，部分数据集和未见节点数量下最高可达6个百分点。iN2V是一种即插即用的方法，可用于生成新嵌入或增强现有嵌入，也可与其他嵌入方法结合，适用于各类归纳式节点表示学习任务。代码已公开于https://github.com/Foisunt/iN2V。
原文摘要 · Abstract (English)
Shallow node embeddings like node2vec (N2V) can be used for nodes without features or to supplement existing features with structure-based information. Embedding methods like N2V are limited in their application on new nodes, which restricts them to the transductive setting where the entire graph, including the test nodes, is available during training. We propose inductive node2vec (iN2V), which combines a post-hoc procedure to compute embeddings for nodes unseen during training and modifications to the original N2V training procedure to prepare the embeddings for this post-hoc procedure. We conduct experiments on several benchmark datasets and demonstrate that iN2V is an effective approach to bringing transductive embeddings to an inductive setting. Using iN2V embeddings improves node classification by 1 point on average, with up to 6 points of improvement depending on the dataset and the number of unseen nodes. Our iN2V is a plug-in approach to create new or enrich existing embeddings. It can also be combined with other embedding methods, making it a versatile approach for inductive node representation learning. Code to reproduce the results is available at https://github.com/Foisunt/iN2V .
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