most citedTree Structure-Aware Graph Representation Learning via Integrated Hierarchical Aggregation and Relational Metric Learning

4 citations · 13 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG20212 cited

Data Augmentation for Graph Convolutional Network on Semi-Supervised Classification

Zhengzheng Tang, Ziyue Qiao, Xuehai Hong +4

Data augmentation aims to generate new and synthetic features from the original data, which can identify a better representation of data and improve the performance and generalizab…

cs.AI20211 cited

LightCAKE: A Lightweight Framework for Context-Aware Knowledge Graph Embedding

Zhiyuan Ning, Ziyue Qiao, Hao Dong +2

Knowledge graph embedding (KGE) models learn to project symbolic entities and relations into a continuous vector space based on the observed triplets. However, existing KGE models…

cs.AI20212 cited

Reinforced Imitative Graph Representation Learning for Mobile User Profiling: An Adversarial Training Perspective

Dongjie Wang, Pengyang Wang, Kunpeng Liu +3

In this paper, we study the problem of mobile user profiling, which is a critical component for quantifying users' characteristics in the human mobility modeling pipeline. Human mo…

cs.CL20204 cited

Context-Enhanced Entity and Relation Embedding for Knowledge Graph Completion

Ziyue Qiao, Zhiyuan Ning, Yi Du +1

Most researches for knowledge graph completion learn representations of entities and relations to predict missing links in incomplete knowledge graphs. However, these methods fail…

cs.SI20204 cited

Tree Structure-Aware Graph Representation Learning via Integrated Hierarchical Aggregation and Relational Metric Learning

Ziyue Qiao, Pengyang Wang, Yanjie Fu +3

While Graph Neural Network (GNN) has shown superiority in learning node representations of homogeneous graphs, leveraging GNN on heterogeneous graphs remains a challenging problem.…