87 citations · 214 across the 7 of their papers we have counts for
7 papers
Dissimilar Nodes Improve Graph Active Learning
Zhicheng Ren, Yifu Yuan, Yuxin Wu +3
Training labels for graph embedding algorithms could be costly to obtain in many practical scenarios. Active learning (AL) algorithms are very helpful to obtain the most useful lab…
Empowering Language Models with Knowledge Graph Reasoning for Question Answering
Ziniu Hu, Yichong Xu, Wenhao Yu +5
Answering open-domain questions requires world knowledge about in-context entities. As pre-trained Language Models (LMs) lack the power to store all required knowledge, external kn…
Relation-Guided Pre-Training for Open-Domain Question Answering
Ziniu Hu, Yizhou Sun, Kai-Wei Chang
Answering complex open-domain questions requires understanding the latent relations between involving entities. However, we found that the existing QA datasets are extremely imbala…
GPT-GNN: Generative Pre-Training of Graph Neural Networks
Ziniu Hu, Yuxiao Dong, Kuansan Wang +2
Graph neural networks (GNNs) have been demonstrated to be powerful in modeling graph-structured data. However, training GNNs usually requires abundant task-specific labeled data, w…
Heterogeneous Graph Transformer
Ziniu Hu, Yuxiao Dong, Kuansan Wang +1
Recent years have witnessed the emerging success of graph neural networks (GNNs) for modeling structured data. However, most GNNs are designed for homogeneous graphs, in which all…
Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks
Difan Zou, Ziniu Hu, Yewen Wang +3
Graph convolutional networks (GCNs) have recently received wide attentions, due to their successful applications in different graph tasks and different domains. Training GCNs for a…