Search Results - "Tan, Shiyin"

Predicting combinations of drugs by exploiting graph embedding of heterogeneous networks by Song, Fei, Tan, Shiyin, Dou, Zengfa, Liu, Xiaogang, Ma, Xiaoke

“…Drug combination, offering an insight into the increased therapeutic efficacy and reduced toxicity, plays an essential role in the therapy of many complex…”
Get full text

Red/deep-red emitting phosphors of Eu3+/Dy3+ co-doped NaSrGd(WO4)3 for LED and optical anti-counterfeiting by Liu, Hong, Li, Wanhang, Ye, Huihua, Hu, Rui, Lin, Jinfeng, Tan, Shiyin, Wang, Xusheng, Li, Yanxia, Yao, Xi

“…The family of rare-earth doped phosphors has drawn pronounced attention in technological of fingerprint visualization, anti-counterfeiting, and…”
Get full text

Joint multi-label learning and feature extraction for temporal link prediction by Ma, Xiaoke, Tan, Shiyin, Xie, Xianghua, Zhong, Xiaoxiong, Deng, Jingjing

“…•We show multi-label learning can be applied to temporal link prediction.•We propose a joint algorithm for temporal link prediction by fusing multilabel…”
Get full text

Detecting evolving communities in dynamic networks using graph regularized evolutionary nonnegative matrix factorization by Ma, Xiaoke, Li, Dongyuan, Tan, Shiyin, Huang, Zhihao

“…Many networks in society and nature are dynamic, and identifying evolving communities in dynamic networks sheds light on revealing the structure and function…”
Get full text

SeHNE: Semi-supervised Heterogeneous Network Embedding for Drug Combination by Tan, Shiyin, Ma, Xiaoke

“…Drug combinations, offering increased therapeutic efficacy and reduced toxicity, play an important role in therapy of many complex diseases. Although great…”
Get full text

Community-Invariant Graph Contrastive Learning by Tan, Shiyin, Li, Dongyuan, Jiang, Renhe, Zhang, Ying, Okumura, Manabu

“…Graph augmentation has received great attention in recent years for graph contrastive learning (GCL) to learn well-generalized node/graph representations…”
Get full text

DyG-Mamba: Continuous State Space Modeling on Dynamic Graphs by Li, Dongyuan, Tan, Shiyin, Zhang, Ying, Jin, Ming, Pan, Shirui, Okumura, Manabu, Jiang, Renhe

“…Dynamic graph learning aims to uncover evolutionary laws in real-world systems, enabling accurate social recommendation (link prediction) or early detection of…”
Get full text