Network embedding is an effective method aiming to learn the low-dimensional vector representation of nodes in networks, which has been widely used in various network analytic tasks such as node classification, node clustering, and link prediction. The objective of network embedding is to capture the structural information and inherent characteristics of the network as much as possible in the low-dimensional vector representation. However, the majority of the existing network embedding methods merely exploited the microscopic proximity of the network structure to learn the node representation, which tend to generate sub-optimal network representation. In this paper, we propose a novel nonnegative matrix factorization (NMF) based network representation learning framework called FLGAI, which jointly integrates the local network structure, global network structure, and attribute information to learn the network representation. First, we employ the first-order proximity and second-order proximity jointly to preserve the local network structure. Then, the community structure is introduced to preserve the global network structure. Third, we exploit the node attribute information to capture the node characteristics. To preserve the structural information and the network node attributes simultaneously, we formulate their consensus relationships and optimize them jointly in a unified NMF framework to derive the final network representation. To evaluate the effectiveness of our model, we conduct extensive experiments on six real-world datasets and the empirical results demonstrate the superior performance of the proposed method over the state-of-the-art approaches in both node classification and node clustering tasks.

网络嵌入是一种旨在学习网络中节点的低维向量表示的有效方法，已广泛用于各种网络分析任务中，例如节点分类，节点聚类和链接预测。网络嵌入的目的是在低维向量表示中尽可能多地捕获网络的结构信息和固有特性。然而，大多数现有的网络嵌入方法仅利用网络结构的微观邻近度来学习节点表示，这倾向于生成次优的网络表示。在本文中，我们提出了一种新颖的基于非负矩阵分解（NMF）的网络表示学习框架，称为FLGAI，该框架共同集成了本地网络结构，全局网络结构和属性信息，以学习网络表示形式。首先，我们共同采用一阶接近度和二阶接近度来保留本地网络结构。然后，引入社区结构以保留全局网络结构。第三，我们利用节点属性信息来捕获节点特征。为了同时保留结构信息和网络节点属性，我们制定了它们的共识关系，并在统一的NMF框架中对其进行了优化，以得出最终的网络表示形式。为了评估我们模型的有效性，