LAUSR

Attributed network embedding enables to generate low-dimensional representations of network objects by leveraging both network structure and attribute data. However, how to properly combine two different information to achieve better vector representations remains still unclear. While some methods learn the embeddings from graph structure and attribute data separately, and then joint them, some existing methods use attribute data as an auxiliary information. However, the problem of integrating attribute data into an embedding process is an open problem due to the sparsity of attribute space. Especially in social networks such as Twitter and Flickr, the contexts may be short and the number of attributes defining objects may be very few, which cause that the contextual proximity among objects are not discovered properly. To address these issues, in this work, we present an enhanced attributed network embedding method via enriched attribute representations (ANEA) which generates low-dimensional representations of the network objects. ANEA incorporates attribute data into the embedding process by mapping the data to two different graph structures. To deal with the sparsity problem, our method provides to capture high-order semantic relations between attributes by performing random walks on these graphs. ANEA learns the embeddings through a joint space composed of the network structure and attributes. Therefore, it allows to discover latent attribute representations of the objects, which is helpful to explain what the common contextual interests are effective in modelling the proximity among nodes. Experiments on real-world networks confirm that ANEA outperforms the state-of-the-art methods.