论文标题
邻居2VEC：图形嵌入的一种有效方法
Neighbor2vec: an efficient and effective method for Graph Embedding
论文作者
论文摘要
近年来，嵌入技术导致了显着的进展。但是，目前的技术不足以捕获网络的模式。本文提出了邻居2VEC，一种基于邻居的采样策略使用算法来学习节点的邻域表示，这是一个通过节点与其邻居之间的特征传播来收集结构信息的框架。我们声称，邻居2VEC是一种简单有效的方法，用于增强嵌入图的可扩展性和平等性，并且打破了现有最新无监督技术的限制。我们对OGBN-ARXIV，OGBN产品，OGBN-蛋白质，OGBL-PPPA，OGBL-Collab和OGBL-Citation等网络进行了几个节点分类和链接预测任务进行实验。结果表明，在节点分类任务中，邻居2VEC的表示的平均精度比竞争方法高达6.8％，链接预测任务中的平均得分高3.0％。在所有六个实验中，邻居2VEC的表示能够胜过所有基线方法和两个经典GNN模型。
Graph embedding techniques have led to significant progress in recent years. However, present techniques are not effective enough to capture the patterns of networks. This paper propose neighbor2vec, a neighbor-based sampling strategy used algorithm to learn the neighborhood representations of node, a framework to gather the structure information by feature propagation between the node and its neighbors. We claim that neighbor2vec is a simple and effective approach to enhancing the scalability as well as equality of graph embedding, and it breaks the limits of the existing state-of-the-art unsupervised techniques. We conduct experiments on several node classification and link prediction tasks for networks such as ogbn-arxiv, ogbn-products, ogbn-proteins, ogbl-ppa,ogbl-collab and ogbl-citation2. The result shows that Neighbor2vec's representations provide an average accuracy scores up to 6.8 percent higher than competing methods in node classification tasks and 3.0 percent higher in link prediction tasks. The neighbor2vec's representations are able to outperform all baseline methods and two classical GNN models in all six experiments.
