337 citations · 823 across the 46 of their papers we have counts for
17 papers · 1 filter
Cluster-guided Contrastive Class-imbalanced Graph Classification
Wei Ju, Zhengyang Mao, Siyu Yi +6
This paper studies the problem of class-imbalanced graph classification, which aims at effectively classifying the graph categories in scenarios with imbalanced class distributions…
DisCo: Graph-Based Disentangled Contrastive Learning for Cold-Start Cross-Domain Recommendation
Hourun Li, Yifan Wang, Zhiping Xiao +4
Recommender systems are widely used in various real-world applications, but they often encounter the persistent challenge of the user cold-start problem. Cross-domain recommendatio…
Embracing Large Language Models in Traffic Flow Forecasting
Yusheng Zhao, Xiao Luo, Haomin Wen +3
Traffic flow forecasting aims to predict future traffic flows based on the historical traffic conditions and the road network. It is an important problem in intelligent transportat…
SMI-Editor: Edit-based SMILES Language Model with Fragment-level Supervision
Kangjie Zheng, Siyue Liang, Junwei Yang +5
SMILES, a crucial textual representation of molecular structures, has garnered significant attention as a foundation for pre-trained language models (LMs). However, most existing p…
GALA: Graph Diffusion-based Alignment with Jigsaw for Source-free Domain Adaptation
Junyu Luo, Yiyang Gu, Xiao Luo +5
Source-free domain adaptation is a crucial machine learning topic, as it contains numerous applications in the real world, particularly with respect to data privacy. Existing appro…
Semi-supervised Fine-tuning for Large Language Models
Junyu Luo, Xiao Luo, Xiusi Chen +3
Supervised fine-tuning (SFT) is crucial in adapting large language model (LLMs) to a specific domain or task. However, only a limited amount of labeled data is available in practic…
Rank and Align: Towards Effective Source-free Graph Domain Adaptation
Junyu Luo, Zhiping Xiao, Yifan Wang +5
Graph neural networks (GNNs) have achieved impressive performance in graph domain adaptation. However, extensive source graphs could be unavailable in real-world scenarios due to p…
DisenSemi: Semi-supervised Graph Classification via Disentangled Representation Learning
Yifan Wang, Xiao Luo, Chong Chen +3
Graph classification is a critical task in numerous multimedia applications, where graphs are employed to represent diverse types of multimedia data, including images, videos, and…
MMEvalPro: Calibrating Multimodal Benchmarks Towards Trustworthy and Efficient Evaluation
Jinsheng Huang, Liang Chen, Taian Guo +13
Large Multimodal Models (LMMs) exhibit impressive cross-modal understanding and reasoning abilities, often assessed through multiple-choice questions (MCQs) that include an image,…
Hypergraph-enhanced Dual Semi-supervised Graph Classification
Wei Ju, Zhengyang Mao, Siyu Yi +6
In this paper, we study semi-supervised graph classification, which aims at accurately predicting the categories of graphs in scenarios with limited labeled graphs and abundant unl…
Towards Graph Contrastive Learning: A Survey and Beyond
Wei Ju, Yifan Wang, Yifang Qin +10
In recent years, deep learning on graphs has achieved remarkable success in various domains. However, the reliance on annotated graph data remains a significant bottleneck due to i…
COOL: A Conjoint Perspective on Spatio-Temporal Graph Neural Network for Traffic Forecasting
Wei Ju, Yusheng Zhao, Yifang Qin +6
This paper investigates traffic forecasting, which attempts to forecast the future state of traffic based on historical situations. This problem has received ever-increasing attent…
A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges
Wei Ju, Siyu Yi, Yifan Wang +10
Graph-structured data exhibits universality and widespread applicability across diverse domains, such as social network analysis, biochemistry, financial fraud detection, and netwo…
A Survey of Data-Efficient Graph Learning
Wei Ju, Siyu Yi, Yifan Wang +4
Graph-structured data, prevalent in domains ranging from social networks to biochemical analysis, serve as the foundation for diverse real-world systems. While graph neural network…
GPS: Graph Contrastive Learning via Multi-scale Augmented Views from Adversarial Pooling
Wei Ju, Yiyang Gu, Zhengyang Mao +5
Self-supervised graph representation learning has recently shown considerable promise in a range of fields, including bioinformatics and social networks. A large number of graph co…
PolyCF: Towards the Optimal Spectral Graph Filters for Collaborative Filtering
Yifang Qin, Wei Ju, Xiao Luo +3
Collaborative Filtering (CF) is a pivotal research area in recommender systems that capitalizes on collaborative similarities between users and items to provide personalized recomm…
Graph Neural Networks in Intelligent Transportation Systems: Advances, Applications and Trends
Hourun Li, Yusheng Zhao, Zhengyang Mao +7
Intelligent Transportation System (ITS) is crucial for improving traffic congestion, reducing accidents, optimizing urban planning, and more. However, the complexity of traffic net…