Community detection is a challenging, yet crucial, problem while mining large-scale graph structured data. Most existing approaches solve this problem by mapping nodes into a vector space and performing unsupervised learning with the resulting embeddings. In cases where multiple types of connectivity patterns exist for the set of nodes, commonly modeled as multilayer graphs, new strategies are required to model the inter-layer dependencies in order to perform effective inferencing. In this paper, we focus on learning embeddings for each node of a multilayer graph through neural modeling techniques, such that the complex dependencies can be concisely encoded into low-dimensional representations. Referred to as multilayer graph embeddings, these representations can be utilized for discovering community structure in a scalable fashion, even with a large number of layers. Furthermore, in order to ensure that the semantics that persist over a longer range in the network are well modeled, we propose to refine the multilayer embeddings via a proxy clustering loss and a graph modularity measure. Using real-world datasets, we demonstrate that this algorithm generates scalable and robust representations, and outperforms existing multilayer community detection approaches.

本文提出了一种新的方法，通过DeepWalk在supra图上共同获得所有层的节点嵌入，并对嵌入进行微调，以在潜在空间中促进凝聚结构，通过节点分类，链接预测和多层社区检测的实证研究表明，该方法在多项基准测试中优于现有的单层和多层网络嵌入算法。

多层网络推理的改进深度嵌入