217 citations · 539 across the 8 of their papers we have counts for
8 papers
Compact Graph Structure Learning via Mutual Information Compression
Nian Liu, Xiao Wang, Lingfei Wu +3
Graph Structure Learning (GSL) recently has attracted considerable attentions in its capacity of optimizing graph structure as well as learning suitable parameters of Graph Neural…
Graph Neural Networks for Natural Language Processing: A Survey
Lingfei Wu, Yu Chen, Kai Shen +5
Deep learning has become the dominant approach in coping with various tasks in Natural LanguageProcessing (NLP). Although text inputs are typically represented as a sequence of tok…
Personalized Food Recommendation as Constrained Question Answering over a Large-scale Food Knowledge Graph
Yu Chen, Ananya Subburathinam, Ching-Hua Chen +1
Food recommendation has become an important means to help guide users to adopt healthy dietary habits. Previous works on food recommendation either i) fail to consider users' expli…
Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings
Yu Chen, Lingfei Wu, Mohammed J. Zaki
In this paper, we propose an end-to-end graph learning framework, namely Iterative Deep Graph Learning (IDGL), for jointly and iteratively learning graph structure and graph embedd…
Toward Subgraph-Guided Knowledge Graph Question Generation with Graph Neural Networks
Yu Chen, Lingfei Wu, Mohammed J. Zaki
Knowledge graph (KG) question generation (QG) aims to generate natural language questions from KGs and target answers. Previous works mostly focus on a simple setting which is to g…
Deep Iterative and Adaptive Learning for Graph Neural Networks
Yu Chen, Lingfei Wu, Mohammed J. Zaki
In this paper, we propose an end-to-end graph learning framework, namely Deep Iterative and Adaptive Learning for Graph Neural Networks (DIAL-GNN), for jointly learning the graph s…