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Attributed network embedding aims to learn low-dimensional noderepresentations from both network structure and node attributes. Existingmethods can be categorized into two groups: (1) the first group learns twoseparated node representations from network structure and node attributerespectively and concatenates them together; (2) the other group obtains noderepresentations by translating node attributes into network structure or viceversa. However, both groups have their drawbacks. The first group neglects thecorrelation between network structure and node attributes, while the secondgroup assumes strong dependence between these two types of information. In thispaper, we address attributed network embedding from a novel perspective, i.e.,learning node context representation for each node via modeling its attributedlocal subgraph. To achieve this goal, we propose a novel attributed networkauto-encoder framework, namely ANAE. For a target node, ANAE first aggregatesthe attribute information from its attributed local subgraph, obtaining itslow-dimensional representation. Next, ANAE diffuses the representation of thetarget node to nodes in its local subgraph to reconstruct their attributes.Such an encoder-decoder framework allows the learned representations to betterpreserve the context information manifested in both network structure and nodeattributes, thus having high capacity to learn good node representations forattributed network. Extensive experimental results on real-world datasetsdemonstrate that the proposed framework outperforms the state-of-the-artapproaches at the tasks of link prediction and node classification.

ANAE: Learning Node Context Representation for Attributed Network Embedding