22 citations · 29 across the 13 of their papers we have counts for
13 papers
Multi-Domain Riemannian Graph Gluing for Building Graph Foundation Models
Li Sun, Zhenhao Huang, Silei Chen +4
Multi-domain graph pre-training integrates knowledge from diverse domains to enhance performance in the target domains, which is crucial for building graph foundation models. Despi…
Learning to Explore: Policy-Guided Outlier Synthesis for Graph Out-of-Distribution Detection
Li Sun, Lanxu Yang, Jiayu Tian +6
Detecting out-of-distribution (OOD) graphs is crucial for ensuring the safety and reliability of Graph Neural Networks. In unsupervised graph-level OOD detection, models are typica…
MoSE: Unveiling Structural Patterns in Graphs via Mixture of Subgraph Experts
Junda Ye, Zhongbao Zhang, Li Sun +1
While graph neural networks (GNNs) have achieved great success in learning from graph-structured data, their reliance on local, pairwise message passing restricts their ability to…
Better Language Model-Based Judging Reward Modeling through Scaling Comprehension Boundaries
Meiling Ning, Zhongbao Zhang, Junda Ye +2
The emergence of LM-based judging reward modeling, represented by generative reward models, has successfully made reinforcement learning from AI feedback (RLAIF) efficient and scal…
Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Survey
Zulun Zhu, Tiancheng Huang, Kai Wang +3
Large language models (LLMs) struggle with the factual error during inference due to the lack of sufficient training data and the most updated knowledge, leading to the hallucinati…
CLEAR: Cluster-based Prompt Learning on Heterogeneous Graphs
Feiyang Wang, Zhongbao Zhang, Junda Ye +2
Prompt learning has attracted increasing attention in the graph domain as a means to bridge the gap between pretext and downstream tasks. Existing studies on heterogeneous graph pr…
DeepRicci: Self-supervised Graph Structure-Feature Co-Refinement for Alleviating Over-squashing
Li Sun, Zhenhao Huang, Hua Wu +4
Graph Neural Networks (GNNs) have shown great power for learning and mining on graphs, and Graph Structure Learning (GSL) plays an important role in boosting GNNs with a refined gr…
Contrastive Sequential Interaction Network Learning on Co-Evolving Riemannian Spaces
Li Sun, Junda Ye, Jiawei Zhang +4
The sequential interaction network usually find itself in a variety of applications, e.g., recommender system. Herein, inferring future interaction is of fundamental importance, an…
SINCERE: Sequential Interaction Networks representation learning on Co-Evolving RiEmannian manifolds
Junda Ye, Zhongbao Zhang, Li Sun +3
Sequential interaction networks (SIN) have been commonly adopted in many applications such as recommendation systems, search engines and social networks to describe the mutual infl…
Contrastive Graph Clustering in Curvature Spaces
Li Sun, Feiyang Wang, Junda Ye +2
Graph clustering is a longstanding research topic, and has achieved remarkable success with the deep learning methods in recent years. Nevertheless, we observe that several importa…
Self-Supervised Continual Graph Learning in Adaptive Riemannian Spaces
Li Sun, Junda Ye, Hao Peng +2
Continual graph learning routinely finds its role in a variety of real-world applications where the graph data with different tasks come sequentially. Despite the success of prior…
A Self-supervised Riemannian GNN with Time Varying Curvature for Temporal Graph Learning
Li Sun, Junda Ye, Hao Peng +1
Representation learning on temporal graphs has drawn considerable research attention owing to its fundamental importance in a wide spectrum of real-world applications. Though a num…
A Self-supervised Mixed-curvature Graph Neural Network
Li Sun, Zhongbao Zhang, Junda Ye +4
Graph representation learning received increasing attentions in recent years. Most of existing methods ignore the complexity of the graph structures and restrict graphs in a single…