93 citations · 181 across the 17 of their papers we have counts for
18 papers
Metacognition as Reward: Reinforcing LLM Reasoning via Knowledge and Regulation Signals
Sirui Chen, Lei Xu, Yuying Zhao +6
Recent RL methods have substantially improved the reasoning abilities of LLMs. Existing reward designs mainly follow two paradigms: (1) Reinforcement learning with verifiable rewar…
Towards Bridging Review Sparsity in Recommendation with Textual Edge Graph Representation
Leyao Wang, Xutao Mao, Xuhui Zhan +5
Textual reviews enrich recommender systems with fine-grained preference signals and enhanced explainability. However, in real-world scenarios, users rarely leave reviews, resulting…
Amplifying Your Social Media Presence: Personalized Influential Content Generation with LLMs
Yuying Zhao, Yu Wang, Xueqi Cheng +5
The remarkable advancements in Large Language Models (LLMs) have revolutionized the content generation process in social media, offering significant convenience in writing tasks. H…
Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Survey
Bo Ni, Zheyuan Liu, Leyao Wang +17
Retrieval-Augmented Generation (RAG) is an advanced technique designed to address the challenges of Artificial Intelligence-Generated Content (AIGC). By integrating context retriev…
BTS: A Comprehensive Benchmark for Tie Strength Prediction
Xueqi Cheng, Catherine Yang, Yuying Zhao +3
The rapid rise of online social networks underscores the need to understand the heterogeneous strengths of online relationships. Yet, efforts to assess tie strength (TS) are hinder…
SaVe-TAG: LLM-based Interpolation for Long-Tailed Text-Attributed Graphs
Leyao Wang, Yu Wang, Bo Ni +4
Real-world graph data often follows long-tailed distributions, making it difficult for Graph Neural Networks (GNNs) to generalize well across both head and tail classes. Recent adv…
PSNE: Efficient Spectral Sparsification Algorithms for Scaling Network Embedding
Longlong Lin, Yunfeng Yu, Zihao Wang +4
Network embedding has numerous practical applications and has received extensive attention in graph learning, which aims at mapping vertices into a low-dimensional and continuous d…
Edge Classification on Graphs: New Directions in Topological Imbalance
Xueqi Cheng, Yu Wang, Yunchao Liu +3
Recent years have witnessed the remarkable success of applying Graph machine learning (GML) to node/graph classification and link prediction. However, edge classification task that…
Augmenting Textual Generation via Topology Aware Retrieval
Yu Wang, Nedim Lipka, Ruiyi Zhang +6
Despite the impressive advancements of Large Language Models (LLMs) in generating text, they are often limited by the knowledge contained in the input and prone to producing inaccu…
Can One Embedding Fit All? A Multi-Interest Learning Paradigm Towards Improving User Interest Diversity Fairness
Yuying Zhao, Minghua Xu, Huiyuan Chen +5
Recommender systems (RSs) have gained widespread applications across various domains owing to the superior ability to capture users' interests. However, the complexity and nuanced…
Leveraging Opposite Gender Interaction Ratio as a Path towards Fairness in Online Dating Recommendations Based on User Sexual Orientation
Yuying Zhao, Yu Wang, Yi Zhang +3
Online dating platforms have gained widespread popularity as a means for individuals to seek potential romantic relationships. While recommender systems have been designed to impro…
A Topological Perspective on Demystifying GNN-Based Link Prediction Performance
Yu Wang, Tong Zhao, Yuying Zhao +4
Graph Neural Networks (GNNs) have shown great promise in learning node embeddings for link prediction (LP). While numerous studies aim to improve the overall LP performance of GNNs…
A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applications
Yi Zhang, Yuying Zhao, Zhaoqing Li +5
Graph Neural Networks (GNNs) have gained significant attention owing to their ability to handle graph-structured data and the improvement in practical applications. However, many o…
Fairness and Diversity in Recommender Systems: A Survey
Yuying Zhao, Yu Wang, Yunchao Liu +3
Recommender systems are effective tools for mitigating information overload and have seen extensive applications across various domains. However, the single focus on utility goals…
Fairness and Explainability: Bridging the Gap Towards Fair Model Explanations
Yuying Zhao, Yu Wang, Tyler Derr
While machine learning models have achieved unprecedented success in real-world applications, they might make biased/unfair decisions for specific demographic groups and hence resu…
Collaboration-Aware Graph Convolutional Network for Recommender Systems
Yu Wang, Yuying Zhao, Yi Zhang +1
Graph Neural Networks (GNNs) have been successfully adopted in recommender systems by virtue of the message-passing that implicitly captures collaborative effect. Nevertheless, mos…
Improving Fairness in Graph Neural Networks via Mitigating Sensitive Attribute Leakage
Yu Wang, Yuying Zhao, Yushun Dong +3
Graph Neural Networks (GNNs) have shown great power in learning node representations on graphs. However, they may inherit historical prejudices from training data, leading to discr…
Imbalanced Graph Classification via Graph-of-Graph Neural Networks
Yu Wang, Yuying Zhao, Neil Shah +1
Graph Neural Networks (GNNs) have achieved unprecedented success in identifying categorical labels of graphs. However, most existing graph classification problems with GNNs follow…