38 citations · 56 across the 12 of their papers we have counts for
13 papers
RDL-Bench: A Benchmark for Task-Aware Question Answering over Relational Databases
Yao Cheng, Siqiang Luo
Relational databases (RDBs) remain the primary infrastructure for storing and managing structured data across enterprise and scientific domains. Natural-language (NL) interfaces to…
What Makes a Desired Graph for Relational Deep Learning?
Yao Cheng, Siqiang Luo
Relational deep learning (RDL) converts relational databases (RDBs) into heterogeneous graphs, but graphs derived directly from database schemas are often not well suited for how g…
Human Cognition Inspired RAG with Knowledge Graph for Complex Problem Solving
Yao Cheng, Yibo Zhao, Jiapeng Zhu +3
Large Language Models (LLMs) have demonstrated significant potential across various domains. However, they often struggle with integrating external knowledge and performing complex…
SEAGraph: Unveiling the Whole Story of Paper Review Comments
Jianxiang Yu, Jiaqi Tan, Zichen Ding +7
Peer review, as a cornerstone of scientific research, ensures the integrity and quality of scholarly work by providing authors with objective feedback for refinement. However, in t…
Can Large Language Models Act as Ensembler for Multi-GNNs?
Hanqi Duan, Yao Cheng, Jianxiang Yu +2
Graph Neural Networks (GNNs) have emerged as powerful models for learning from graph-structured data. However, GNNs lack the inherent semantic understanding capability of rich text…
Boosting Graph Foundation Model from Structural Perspective
Yao Cheng, Yige Zhao, Jianxiang Yu +1
Graph foundation models have recently attracted significant attention due to its strong generalizability. Although existing methods resort to language models to learn unified seman…
Improving Graph Out-of-distribution Generalization Beyond Causality
Can Xu, Yao Cheng, Jianxiang Yu +4
Existing methods for graph out-of-distribution (OOD) generalization primarily rely on empirical studies on synthetic datasets. Such approaches tend to overemphasize the causal rela…
A Survey on Learning from Graphs with Heterophily: Recent Advances and Future Directions
Chenghua Gong, Yao Cheng, Jianxiang Yu +4
Graphs are structured data that models complex relations between real-world entities. Heterophilic graphs, where linked nodes are prone to be with different labels or dissimilar fe…
Prioritized Propagation in Graph Neural Networks
Yao Cheng, Minjie Chen, Xiang Li +2
Graph neural networks (GNNs) have recently received significant attention. Learning node-wise message propagation in GNNs aims to set personalized propagation steps for different n…
Variational Graph Autoencoder for Heterogeneous Information Networks with Missing and Inaccurate Attributes
Yige Zhao, Jianxiang Yu, Yao Cheng +4
Heterogeneous Information Networks (HINs), which consist of various types of nodes and edges, have recently demonstrated excellent performance in graph mining. However, most existi…
Resurrecting Label Propagation for Graphs with Heterophily and Label Noise
Yao Cheng, Caihua Shan, Yifei Shen +3
Label noise is a common challenge in large datasets, as it can significantly degrade the generalization ability of deep neural networks. Most existing studies focus on noisy labels…
Graph Self-Contrast Representation Learning
Minjie Chen, Yao Cheng, Ye Wang +2
Graph contrastive learning (GCL) has recently emerged as a promising approach for graph representation learning. Some existing methods adopt the 1-vs-K scheme to construct one posi…
Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
Xiang Li, Renyu Zhu, Yao Cheng +4
We investigate graph neural networks on graphs with heterophily. Some existing methods amplify a node's neighborhood with multi-hop neighbors to include more nodes with homophily.…