82 citations · 189 across the 21 of their papers we have counts for
22 papers
Graph Cross-Correlated Network for Recommendation
Hao Chen, Yuanchen Bei, Wenbing Huang +3
Collaborative filtering (CF) models have demonstrated remarkable performance in recommender systems, which represent users and items as embedding vectors. Recently, due to the powe…
Logical Reasoning with Relation Network for Inductive Knowledge Graph Completion
Qinggang Zhang, Keyu Duan, Junnan Dong +2
Inductive knowledge graph completion (KGC) aims to infer the missing relation for a set of newly-coming entities that never appeared in the training set. Such a setting is more in…
Denoising-Aware Contrastive Learning for Noisy Time Series
Shuang Zhou, Daochen Zha, Xiao Shen +3
Time series self-supervised learning (SSL) aims to exploit unlabeled data for pre-training to mitigate the reliance on labels. Despite the great success in recent years, there is l…
Entity Alignment with Noisy Annotations from Large Language Models
Shengyuan Chen, Qinggang Zhang, Junnan Dong +3
Entity alignment (EA) aims to merge two knowledge graphs (KGs) by identifying equivalent entity pairs. While existing methods heavily rely on human-generated labels, it is prohibit…
Cost-efficient Knowledge-based Question Answering with Large Language Models
Junnan Dong, Qinggang Zhang, Chuang Zhou +3
Knowledge-based question answering (KBQA) is widely used in many scenarios that necessitate domain knowledge. Large language models (LLMs) bring opportunities to KBQA, while their…
Configurable Mirror Descent: Towards a Unification of Decision Making
Pengdeng Li, Shuxin Li, Chang Yang +5
Decision-making problems, categorized as single-agent, e.g., Atari, cooperative multi-agent, e.g., Hanabi, competitive multi-agent, e.g., Hold'em poker, and mixed cooperative and c…
Modality-Aware Integration with Large Language Models for Knowledge-based Visual Question Answering
Junnan Dong, Qinggang Zhang, Huachi Zhou +3
Knowledge-based visual question answering (KVQA) has been extensively studied to answer visual questions with external knowledge, e.g., knowledge graphs (KGs). While several attemp…
Knowledge-to-SQL: Enhancing SQL Generation with Data Expert LLM
Zijin Hong, Zheng Yuan, Hao Chen +3
Generating accurate SQL queries for user questions (text-to-SQL) has been a long-standing challenge since it requires a deep understanding of both the user's question and the corre…
Multi-Behavior Collaborative Filtering with Partial Order Graph Convolutional Networks
Yijie Zhang, Yuanchen Bei, Hao Chen +6
Representing information of multiple behaviors in the single graph collaborative filtering (CF) vector has been a long-standing challenge. This is because different behaviors natur…
Macro Graph Neural Networks for Online Billion-Scale Recommender Systems
Hao Chen, Yuanchen Bei, Qijie Shen +6
Predicting Click-Through Rate (CTR) in billion-scale recommender systems poses a long-standing challenge for Graph Neural Networks (GNNs) due to the overwhelming computational comp…
KnowGPT: Knowledge Graph based Prompting for Large Language Models
Qinggang Zhang, Junnan Dong, Hao Chen +3
Large Language Models (LLMs) have demonstrated remarkable capabilities in many real-world applications. Nonetheless, LLMs are often criticized for their tendency to produce halluci…
Collaborative Graph Neural Networks for Attributed Network Embedding
Qiaoyu Tan, Xin Zhang, Xiao Huang +3
Graph neural networks (GNNs) have shown prominent performance on attributed network embedding. However, existing efforts mainly focus on exploiting network structures, while the ex…
Could Small Language Models Serve as Recommenders? Towards Data-centric Cold-start Recommendations
Xuansheng Wu, Huachi Zhou, Yucheng Shi +3
Recommendation systems help users find matched items based on their previous behaviors. Personalized recommendation becomes challenging in the absence of historical user-item inter…
MolCPT: Molecule Continuous Prompt Tuning to Generalize Molecular Representation Learning
Cameron Diao, Kaixiong Zhou, Zirui Liu +2
Molecular representation learning is crucial for the problem of molecular property prediction, where graph neural networks (GNNs) serve as an effective solution due to their struct…
QuanGCN: Noise-Adaptive Training for Robust Quantum Graph Convolutional Networks
Kaixiong Zhou, Zhenyu Zhang, Shengyuan Chen +4
Quantum neural networks (QNNs), an interdisciplinary field of quantum computing and machine learning, have attracted tremendous research interests due to the specific quantum advan…
RSC: Accelerating Graph Neural Networks Training via Randomized Sparse Computations
Zirui Liu, Shengyuan Chen, Kaixiong Zhou +3
The training of graph neural networks (GNNs) is extremely time consuming because sparse graph-based operations are hard to be accelerated by hardware. Prior art explores trading of…
Graph Contrastive Learning with Personalized Augmentation
Xin Zhang, Qiaoyu Tan, Xiao Huang +1
Graph contrastive learning (GCL) has emerged as an effective tool for learning unsupervised representations of graphs. The key idea is to maximize the agreement between two augment…
Towards Deeper Graph Neural Networks with Differentiable Group Normalization
Kaixiong Zhou, Xiao Huang, Yuening Li +3
Graph neural networks (GNNs), which learn the representation of a node by aggregating its neighbors, have become an effective computational tool in downstream applications. Over-sm…
Auto-GNN: Neural Architecture Search of Graph Neural Networks
Kaixiong Zhou, Qingquan Song, Xiao Huang +1
Graph neural networks (GNN) has been successfully applied to operate on the graph-structured data. Given a specific scenario, rich human expertise and tremendous laborious trials a…
SpecAE: Spectral AutoEncoder for Anomaly Detection in Attributed Networks
Yuening Li, Xiao Huang, Jundong Li +2
Anomaly detection aims to distinguish observations that are rare and different from the majority. While most existing algorithms assume that instances are i.i.d., in many practical…
Multi-Label Adversarial Perturbations
Qingquan Song, Haifeng Jin, Xiao Huang +1
Adversarial examples are delicately perturbed inputs, which aim to mislead machine learning models towards incorrect outputs. While most of the existing work focuses on generating…
Index Coding and Network Coding via Rank Minimization
Xiao Huang, Salim El Rouayheb
Index codes reduce the number of bits broadcast by a wireless transmitter to a number of receivers with different demands and with side information. It is known that the problem of…