Chong Chen

TGNN: A Joint Semi-supervised Framework for Graph-level Classification

2022

This paper studies semi-supervised graph classification, a crucial task with a wide range of applications in social network analysis and bioinformatics. Recent works typically adopt graph neural networks to learn graph-level representations for classification, failing …

SPORT: A Subgraph Perspective on Graph Classification with Label Noise

2024 · ACM Transactions on Knowledge Discovery from Data

Graph neural networks (GNNs) have achieved great success recently on graph classification tasks using supervised end-to-end training. Unfortunately, extensive noisy graph labels could exist in the real world because of the complicated processes of manual …

MRAMG-Bench: A Comprehensive Benchmark for Advancing Multimodal Retrieval-Augmented Multimodal Generation

2025 · arXiv (Cornell University)

Recent advances in Retrieval-Augmented Generation (RAG) have significantly improved response accuracy and relevance by incorporating external knowledge into Large Language Models (LLMs). However, existing RAG methods primarily focus on generating text-only answers, even in Multimodal …

Privacy-Preserving Rényi Layer-Wise Budget Allocation Against Gradient Leakage for Federated Learning

2025 · IEEE Transactions on Mobile Computing

Federated learning (FL) is vulnerable to gradient-based privacy attacks, where malicious attackers reconstruct training data from exchanged gradients. While existing differential privacy (DP) defenses mitigate this, they often cause excessive additive noise due to the …

Deep Cut-informed Graph Embedding and Clustering

2025 · arXiv (Cornell University)

Graph clustering aims to divide the graph into different clusters. The recently emerging deep graph clustering approaches are largely built on graph neural networks (GNN). However, GNN is designed for general graph encoding and there …

Neural Attentional Rating Regression with Review-level Explanations

2018

Reviews information is dominant for users to make online purchasing decisions in e-commerces. However, the usefulness of reviews is varied. We argue that less-useful reviews hurt model's performance, and are also less meaningful for user's …

Social Attentional Memory Network

2019

Social connections are known to be helpful for modeling users' potential preferences and improving the performance of recommender systems. However, in social-aware recommendations, there are two issues which influence the inference of users' preferences, and …

An Efficient Adaptive Transfer Neural Network for Social-aware Recommendation

2019

Many previous studies attempt to utilize information from other domains to achieve better performance of recommendation. Recently, social information has been shown effective in improving recommendation results with transfer learning frameworks, and the transfer part …

Efficient Neural Matrix Factorization without Sampling for Recommendation

2020 · ACM Transactions on Information Systems

Recommendation systems play a vital role to keep users engaged with personalized contents in modern online platforms. Recently, deep learning has revolutionized many research fields and there is a surge of interest in applying it …