Mapping nodes in multi-layer and heterogeneous networks to low-dimensional vectors has wide applications in community detection, node classification and link prediction, etc. In this paper, a generalized graph representation learning framework is proposed for information aggregation in various multi-layer and heterogeneous networks. Specifically, an aggregation network is firstly obtained by graph transformation, generating potential information links based on the network structure on different layers. A comprehensive measurement of the similarity between different nodes in the aggregation network is then carried out by aggregating the information of nodes’ identities of structure, nearness and attributes etc. Based on the comprehensive similarity values the nodes have, a context graph can be generated using a simple edge percolation method, which provides a basis facilitating some important downstream work such as classification, clustering and prediction etc. We demonstrate the effectiveness of the new framework in identifying subnetworks in a cyberspace network, where it significantly outperforms all the existing baselines.

将多层异构网络中的节点映射到低维向量在社区检测、节点分类和链接预测等方面具有广泛的应用。本文提出了一种广义的图表示学习框架，用于各种多层和异构网络中的信息聚合。异构网络。具体来说，首先通过图变换得到一个聚合网络，根据网络结构生成潜在的信息链接在不同的图层上。然后通过聚合节点的结构、接近度和属性等身份信息，对聚合网络中不同节点之间的相似度进行综合度量。 基于节点具有的综合相似度值，可以使用以下方法生成上下文图一种简单的边缘渗透方法，它为一些重要的下游工作（如分类、聚类和预测等）提供了基础。我们证明了新框架在识别网络空间网络中的子网络方面的有效性，它显着优于所有现有基线。