588 citations · 614 across the 6 of their papers we have counts for
6 papers
r-GAT: Relational Graph Attention Network for Multi-Relational Graphs
Meiqi Chen, Yuan Zhang, Xiaoyu Kou +2
Graph Attention Network (GAT) focuses on modelling simple undirected and single relational graph data only. This limits its ability to deal with more general and complex multi-rela…
Disentangle-based Continual Graph Representation Learning
Xiaoyu Kou, Yankai Lin, Shaobo Liu +3
Graph embedding (GE) methods embed nodes (and/or edges) in graph into a low-dimensional semantic space, and have shown its effectiveness in modeling multi-relational data. However,…
DisenE: Disentangling Knowledge Graph Embeddings
Xiaoyu Kou, Yankai Lin, Yuntao Li +4
Knowledge graph embedding (KGE), aiming to embed entities and relations into low-dimensional vectors, has attracted wide attention recently. However, the existing research is mainl…
NASE: Learning Knowledge Graph Embedding for Link Prediction via Neural Architecture Search
Xiaoyu Kou, Bingfeng Luo, Huang Hu +1
Link prediction is the task of predicting missing connections between entities in the knowledge graph (KG). While various forms of models are proposed for the link prediction task,…
TextNAS: A Neural Architecture Search Space tailored for Text Representation
Yujing Wang, Yaming Yang, Yiren Chen +7
Learning text representation is crucial for text classification and other language related tasks. There are a diverse set of text representation networks in the literature, and how…
Time-Series Anomaly Detection Service at Microsoft
Hansheng Ren, Bixiong Xu, Yujing Wang +7
Large companies need to monitor various metrics (for example, Page Views and Revenue) of their applications and services in real time. At Microsoft, we develop a time-series anomal…