论文标题
用扩散图的快速基于序列的嵌入
Fast Sequence-Based Embedding with Diffusion Graphs
论文作者
论文摘要
图形嵌入是在低维空间中图形顶点的表示，该图形大约保留了诸如节点之间的距离之类的属性。基于顶点序列的嵌入过程使用从节点线性序列提取的特征来使用神经网络创建嵌入。在本文中，我们提出扩散图作为快速生成网络嵌入顶点序列的方法。由于序列的生成更简单，它的计算效率优于以前的方法，并且产生了更准确的结果。在实验中，我们发现相对于其他方法的性能随图表中的边缘密度的增加而提高。在社区检测任务中，与其他基于序列的嵌入方法相比，嵌入空间中的聚类节点会产生更好的结果。
A graph embedding is a representation of graph vertices in a low-dimensional space, which approximately preserves properties such as distances between nodes. Vertex sequence-based embedding procedures use features extracted from linear sequences of nodes to create embeddings using a neural network. In this paper, we propose diffusion graphs as a method to rapidly generate vertex sequences for network embedding. Its computational efficiency is superior to previous methods due to simpler sequence generation, and it produces more accurate results. In experiments, we found that the performance relative to other methods improves with increasing edge density in the graph. In a community detection task, clustering nodes in the embedding space produces better results compared to other sequence-based embedding methods.
