Type Sequence Preserving Heterogeneous Information Network Embedding - Chen, Yuxin; Wang, Tengjiao; Chen, Wei; Li, Qiang; Qiu, Zhen (Proceedings of the AAAI Conference on Artificial Intelligence, 2019)
Lacking in sequence preserving mechanism, existing heterogeneous information network (HIN) embedding discards the essential type sequence information during embedding. We propose a Type Sequence Preserving HIN Embedding model (SeqHINE) which expands the HIN embedding to sequence level. SeqHINE incorporates the type sequence information via type-aware GRU and preserves representative sequence information by decay function. Abundant experiments show that SeqHINE can outperform state-of-the-art even with 50% less labeled data.