87 citations · 343 across the 20 of their papers we have counts for
21 papers
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…
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…
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…
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…
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…
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…
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…
A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability
Enyan Dai, Tianxiang Zhao, Huaisheng Zhu +5
Graph Neural Networks (GNNs) have made rapid developments in the recent years. Due to their great ability in modeling graph-structured data, GNNs are vastly used in various applica…
Learning Fair Models without Sensitive Attributes: A Generative Approach
Huaisheng Zhu, Enyan Dai, Hui Liu +1
Most existing fair classifiers rely on sensitive attributes to achieve fairness. However, for many scenarios, we cannot obtain sensitive attributes due to privacy and legal issues.…
Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels
Enyan Dai, Wei Jin, Hui Liu +1
Graph Neural Networks (GNNs) have shown their great ability in modeling graph structured data. However, real-world graphs usually contain structure noises and have limited labeled…
Toward Annotator Group Bias in Crowdsourcing
Haochen Liu, Joseph Thekinen, Sinem Mollaoglu +5
Crowdsourcing has emerged as a popular approach for collecting annotated data to train supervised machine learning models. However, annotator bias can lead to defective annotations…
AutoLoss: Automated Loss Function Search in Recommendations
Xiangyu Zhao, Haochen Liu, Wenqi Fan +3
Designing an effective loss function plays a crucial role in training deep recommender systems. Most existing works often leverage a predefined and fixed loss function that could l…
Mitigating Gender Bias for Neural Dialogue Generation with Adversarial Learning
Haochen Liu, Wentao Wang, Yiqi Wang +3
Dialogue systems play an increasingly important role in various aspects of our daily life. It is evident from recent research that dialogue systems trained on human conversation da…
Multi-Scale One-Class Recurrent Neural Networks for Discrete Event Sequence Anomaly Detection
Zhiwei Wang, Zhengzhang Chen, Jingchao Ni +3
Discrete event sequences are ubiquitous, such as an ordered event series of process interactions in Information and Communication Technology systems. Recent years have witnessed in…
Memory-efficient Embedding for Recommendations
Xiangyu Zhao, Haochen Liu, Hui Liu +6
Practical large-scale recommender systems usually contain thousands of feature fields from users, items, contextual information, and their interactions. Most of them empirically al…
Attacking Black-box Recommendations via Copying Cross-domain User Profiles
Wenqi Fan, Tyler Derr, Xiangyu Zhao +5
Recently, recommender systems that aim to suggest personalized lists of items for users to interact with online have drawn a lot of attention. In fact, many of these state-of-the-a…
Learning Multi-level Dependencies for Robust Word Recognition
Zhiwei Wang, Hui Liu, Jiliang Tang +3
Robust language processing systems are becoming increasingly important given the recent awareness of dangerous situations where brittle machine learning models can be easily broken…
Adversarial Attacks and Defenses in Images, Graphs and Text: A Review
Han Xu, Yao Ma, Haochen Liu +4
Deep neural networks (DNN) have achieved unprecedented success in numerous machine learning tasks in various domains. However, the existence of adversarial examples has raised conc…
Does Gender Matter? Towards Fairness in Dialogue Systems
Haochen Liu, Jamell Dacon, Wenqi Fan +3
Recently there are increasing concerns about the fairness of Artificial Intelligence (AI) in real-world applications such as computer vision and recommendations. For example, recog…
DEAR: Deep Reinforcement Learning for Online Advertising Impression in Recommender Systems
Xiangyu Zhao, Changsheng Gu, Haoshenglun Zhang +4
With the recent prevalence of Reinforcement Learning (RL), there have been tremendous interests in utilizing RL for online advertising in recommendation platforms (e.g., e-commerce…
Whole-Chain Recommendations
Xiangyu Zhao, Long Xia, Linxin Zou +3
With the recent prevalence of Reinforcement Learning (RL), there have been tremendous interests in developing RL-based recommender systems. In practical recommendation sessions, us…