24 citations · 52 across the 6 of their papers we have counts for
8 papers · 1 filter
Efficient User Sequence Learning for Online Services via Compressed Graph Neural Networks
Yucheng Wu, Liyue Chen, Yu Cheng +3
Learning representations of user behavior sequences is crucial for various online services, such as online fraudulent transaction detection mechanisms. Graph Neural Networks (GNNs)…
A Unified Model for Spatio-Temporal Prediction Queries with Arbitrary Modifiable Areal Units
Liyue Chen, Jiangyi Fang, Tengfei Liu +2
Spatio-Temporal (ST) prediction is crucial for making informed decisions in urban location-based applications like ride-sharing. However, existing ST models often require region pa…
Graph Contrastive Learning with Cohesive Subgraph Awareness
Yucheng Wu, Leye Wang, Xiao Han +1
Graph contrastive learning (GCL) has emerged as a state-of-the-art strategy for learning representations of diverse graphs including social and biomedical networks. GCL widely uses…
Knowledge-inspired Subdomain Adaptation for Cross-Domain Knowledge Transfer
Liyue Chen, Linian Wang, Jinyu Xu +5
Most state-of-the-art deep domain adaptation techniques align source and target samples in a global fashion. That is, after alignment, each source sample is expected to become simi…
A Data-driven Region Generation Framework for Spatiotemporal Transportation Service Management
Liyue Chen, Jiangyi Fang, Zhe Yu +3
MAUP (modifiable areal unit problem) is a fundamental problem for spatial data management and analysis. As an instantiation of MAUP in online transportation platforms, region gener…
A Survey on Vertical Federated Learning: From a Layered Perspective
Liu Yang, Di Chai, Junxue Zhang +6
Vertical federated learning (VFL) is a promising category of federated learning for the scenario where data is vertically partitioned and distributed among parties. VFL enriches th…