24 citations · 76 across the 12 of their papers we have counts for
12 papers
Identifying and Correcting Label Noise for Robust GNNs via Influence Contradiction
Wei Ju, Wei Zhang, Siyu Yi +6
Graph Neural Networks (GNNs) have shown remarkable capabilities in learning from graph-structured data with various applications such as social analysis and bioinformatics. However…
AlphaEval: A Comprehensive and Efficient Evaluation Framework for Formula Alpha Mining
Hongjun Ding, Binqi Chen, Jinsheng Huang +6
Formula alpha mining, which generates predictive signals from financial data, is critical for quantitative investment. Although various algorithmic approaches-such as genetic progr…
MASS: Muli-agent simulation scaling for portfolio construction
Taian Guo, Haiyang Shen, JinSheng Huang +9
The application of LLM-based agents in financial investment has shown significant promise, yet existing approaches often require intermediate steps like predicting individual stock…
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…
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…
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…
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…
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…
ALEX: Towards Effective Graph Transfer Learning with Noisy Labels
Jingyang Yuan, Xiao Luo, Yifang Qin +3
Graph Neural Networks (GNNs) have garnered considerable interest due to their exceptional performance in a wide range of graph machine learning tasks. Nevertheless, the majority of…
RAHNet: Retrieval Augmented Hybrid Network for Long-tailed Graph Classification
Zhengyang Mao, Wei Ju, Yifang Qin +2
Graph classification is a crucial task in many real-world multimedia applications, where graphs can represent various multimedia data types such as images, videos, and social netwo…
Towards Long-Tailed Recognition for Graph Classification via Collaborative Experts
Siyu Yi, Zhengyang Mao, Wei Ju +4
Graph classification, aiming at learning the graph-level representations for effective class assignments, has received outstanding achievements, which heavily relies on high-qualit…