activity
20212024
most citedA Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT

156 citations · 175 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL2024

Heterogeneous Subgraph Transformer for Fake News Detection

Yuchen Zhang, Xiaoxiao Ma, Jia Wu +2

Fake news is pervasive on social media, inflicting substantial harm on public discourse and societal well-being. We investigate the explicit structural information and textual feat…

cs.AI20232 cited

KGTrust: Evaluating Trustworthiness of SIoT via Knowledge Enhanced Graph Neural Networks

Zhizhi Yu, Di Jin, Cuiying Huo +5

Social Internet of Things (SIoT), a promising and emerging paradigm that injects the notion of social networking into smart objects (i.e., things), paving the way for the next gene…

cs.IR202312 cited

A Comprehensive Survey on Automatic Knowledge Graph Construction

Lingfeng Zhong, Jia Wu, Qian Li +2

Automatic knowledge graph construction aims to manufacture structured human knowledge. To this end, much effort has historically been spent extracting informative fact patterns fro…

cs.AI2023156 cited

A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT

Ce Zhou, Qian Li, Chen Li +16

Pretrained Foundation Models (PFMs) are regarded as the foundation for various downstream tasks with different data modalities. A PFM (e.g., BERT, ChatGPT, and GPT-4) is trained on…

cs.LG20235 cited

Unbiased and Efficient Self-Supervised Incremental Contrastive Learning

Cheng Ji, Jianxin Li, Hao Peng +4

Contrastive Learning (CL) has been proved to be a powerful self-supervised approach for a wide range of domains, including computer vision and graph representation learning. Howeve…

cs.LG2021

Graph Structure Learning with Variational Information Bottleneck

Qingyun Sun, Jianxin Li, Hao Peng +4

Graph Neural Networks (GNNs) have shown promising results on a broad spectrum of applications. Most empirical studies of GNNs directly take the observed graph as input, assuming th…