Structural identity is a concept of symmetry in which network nodes are identified according to the network structure and their relationship to other nodes. Structural identity has been studied in theory and practice over the past decades, but has only recently been addressed with techniques from representational learning. This work presents struc2vec, a novel and flexible framework for learning latent representations of node's structural identity. struc2vec assesses structural similarity without using node or edge attributes, uses a hierarchy to measure similarity at different scales, and constructs a multilayer graph to encode the structural similarities and generate structural context for nodes. Numerical experiments indicate that state-of-the-art techniques for learning node representations fail in capturing stronger notions of structural identity, while struc2vec exhibits much superior performance in this task, as it overcomes limitations of prior techniques.

本研究介绍了一种用于学习节点结构结构标识的潜在表示的新型灵活框架 struc2vec，它使用分层来在不同尺度上测量节点相似性，并构建多层图来编码结构相似性和生成节点的结构上下文，数值实验表明其在节点表示的任务中表现优异。

struc2vec：从结构标识学习节点表示