787 citations · 2.6k across the 23 of their papers we have counts for
8 papers · 1 filter
Learning Large-scale Network Embedding from Representative Subgraph
Junsheng Kong, Weizhao Li, Ben Liao +6
We study the problem of large-scale network embedding, which aims to learn low-dimensional latent representations for network mining applications. Recent research in the field of n…
Stable Prediction on Graphs with Agnostic Distribution Shift
Shengyu Zhang, Kun Kuang, Jiezhong Qiu +5
Graph is a flexible and effective tool to represent complex structures in practice and graph neural networks (GNNs) have been shown to be effective on various graph tasks with rand…
SketchNE: Embedding Billion-Scale Networks Accurately in One Hour
Yuyang Xie, Yuxiao Dong, Jiezhong Qiu +3
We study large-scale network embedding with the goal of generating high-quality embeddings for networks with more than 1 billion vertices and 100 billion edges. Recent attempts Lig…
Fast Extraction of Word Embedding from Q-contexts
Junsheng Kong, Weizhao Li, Zeyi Liu +5
The notion of word embedding plays a fundamental role in natural language processing (NLP). However, pre-training word embedding for very large-scale vocabulary is computationally…
Modeling Protein Using Large-scale Pretrain Language Model
Yijia Xiao, Jiezhong Qiu, Ziang Li +2
Protein is linked to almost every life process. Therefore, analyzing the biological structure and property of protein sequences is critical to the exploration of life, as well as d…
Pre-Trained Models: Past, Present and Future
Xu Han, Zhengyan Zhang, Ning Ding +21
Large-scale pre-trained models (PTMs) such as BERT and GPT have recently achieved great success and become a milestone in the field of artificial intelligence (AI). Owing to sophis…