Title: SOLAR: Fusing Node Embeddings and Attributes into an Arbitrary Space
Authors: Zheng Wang, Jian Cui, Yingying Chen and Changjun Hu
Abstract: Network embedding has attracted lots of attention in recent years. It learns low-dimensional representations for network nodes, which benefits many downstream tasks such as node classification and link prediction. However, most of the existing approaches are designed for a single network scenario. In the era of big data, the related information from different networks should be fused together to facilitate applications. In this paper, we study the problem of fusing the node embeddings and incomplete node attributes provided by different networks into an arbitrary space. Specifically, we first propose a simple but effective inductive method by learning the relationships among node embeddings and the given attributes. Then, we propose its transductive variant by jointly considering the node embeddings and incomplete attributes. Finally, we introduce its deep transductive variant based on deep AutoEncoder. Experimental results on four datasets demonstrate the superiority of our methods.
