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20192023
most citedA Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability

87 citations · 343 across the 20 of their papers we have counts for

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Showing 2023Show all

7 papers · 1 filter

cs.LG2023★ 20 cited

Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs

Zhikai Chen, Haitao Mao, Hang Li +8

Learning on Graphs has attracted immense attention due to its wide real-world applications. The most popular pipeline for learning on graphs with textual node attributes primarily…

cs.CL2023★ 64 cited

Empowering Molecule Discovery for Molecule-Caption Translation with Large Language Models: A ChatGPT Perspective

Jiatong Li, Yunqing Liu, Wenqi Fan +4

Molecule discovery plays a crucial role in various scientific fields, advancing the design of tailored materials and drugs. However, most of the existing methods heavily rely on do…

cs.CR2023★ 12 cited

DiffusionShield: A Watermark for Copyright Protection against Generative Diffusion Models

Yingqian Cui, Jie Ren, Han Xu +5

Recently, Generative Diffusion Models (GDMs) have showcased their remarkable capabilities in learning and generating images. A large community of GDMs has naturally emerged, furthe…

cs.CR2023

Sharpness-Aware Data Poisoning Attack

Pengfei He, Han Xu, Jie Ren +4

Recent research has highlighted the vulnerability of Deep Neural Networks (DNNs) against data poisoning attacks. These attacks aim to inject poisoning samples into the models' trai…

cs.LG2023★ 14 cited

Counterfactual Learning on Graphs: A Survey

Zhimeng Guo, Teng Xiao, Zongyu Wu +3

Graph-structured data are pervasive in the real-world such as social networks, molecular graphs and transaction networks. Graph neural networks (GNNs) have achieved great success i…

q-bio.GN2023★ 21 cited

Single-Cell Multimodal Prediction via Transformers

Wenzhuo Tang, Hongzhi Wen, Renming Liu +5

The recent development of multimodal single-cell technology has made the possibility of acquiring multiple omics data from individual cells, thereby enabling a deeper understanding…

cs.LG2023★ 9 cited

Generative Diffusion Models on Graphs: Methods and Applications

Chengyi Liu, Wenqi Fan, Yunqing Liu +5

Diffusion models, as a novel generative paradigm, have achieved remarkable success in various image generation tasks such as image inpainting, image-to-text translation, and video…