Traditionally, community detection and network embedding are two separate tasks. Network embedding aims to output a vector representation for each node in the network, and community detection aims to find all densely connected groups of nodes and well separate them from others. Most of the existing approaches do community detection and network embedding in a separate manner, and ignore node attributes information, which leads to poor results. In this paper, we propose a novel model that jointly solves the network embedding and community detection problems together. The model can make use of the network local information, the global information and node attributes information collaboratively. We empirically show that by jointly solving these two problems together, the model can greatly improve the ability of community detection, but also learn better network embedding than the advanced baseline methods. We evaluate the proposed model on several datasets, and the experimental results have shown the effectiveness and advancement of our model.

传统上，社区检测和网络嵌入是两个单独的任务。网络嵌入的目的是为网络中的每个节点输出向量表示，而社区检测的目的是找到所有密集连接的节点组并将它们与其他节点很好地分开。大多数现有方法以单独的方式进行社区检测和网络嵌入，并且忽略节点属性信息，这会导致较差的结果。在本文中，我们提出了一种新颖的模型，可以共同解决网络嵌入和社区检测问题。该模型可以协同使用网络本地信息，全局信息和节点属性信息。我们的经验表明，通过共同解决这两个问题，该模型可以大大提高社区发现的能力，而且还比高级基准方法学到了更好的网络嵌入。我们在几个数据集上对提出的模型进行了评估，实验结果表明了该模型的有效性和先进性。