@article{Chen_Wang_Chen_Li_Qiu_2019, title={Type Sequence Preserving Heterogeneous Information Network Embedding}, volume={33}, url={https://ojs.aaai.org/index.php/AAAI/article/view/5102}, DOI={10.1609/aaai.v33i01.33019931}, abstractNote={<p>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 <em>type-aware GRU</em> and preserves representative sequence information by <em>decay function</em>. Abundant experiments show that SeqHINE can outperform state-of-the-art even with 50% less labeled data.</p>}, number={01}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Chen, Yuxin and Wang, Tengjiao and Chen, Wei and Li, Qiang and Qiu, Zhen}, year={2019}, month={Jul.}, pages={9931-9932} }