FLGAI: a unified network embedding framework integrating multi-scale network structures and node attribute information

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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.