Inductive Embedding Learning on Attributed Heterogeneous Networks via Multi-task Sequence-to-Sequence Learning

In the paper, we study the problem of inductive embedding learning on attributed heterogeneous networks, and propose a Multi-task sequence-to-sequence learning based Inductive Network Embedding framework (MINE) capturing the attribute similarity, network proximity, and partial label information simultaneously. In particular, MINE trains an encoder function that aggregates information from a node's long-range scope of contexts, with the node attribute sequences generated by the proposed type-guided heterogeneous random walk as inputs. We present an one-to-many multi-task sequence-to-sequence model where the encoder is shared between two related tasks: an unsupervised node identity sequence generation task to learn context-aware embeddings, and a semi-supervised label prediction task to learn semantics-rich embeddings. Extensive experiments on real-world datasets demonstrate that the proposed method significantly outperforms several state-of-the-art methods.