14 citations · 19 across the 18 of their papers we have counts for
16 papers · 1 filter
Uncovering Semantic Hierarchies in Text-Attributed Graphs via Variational EM-based LLM-GNN Synergy
Yunhui Liu, Xudong Jin, Qizhuo Xie +5
While hierarchical structures are intrinsic to organizing human knowledge, current representation learning on text-attributed graphs predominantly operates on flat semantic spaces,…
A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions
Zhiyin Yu, Yuchen Mou, Juncheng Yan +17
Reinforcement learning (RL) has emerged as a powerful post-training paradigm for enhancing the reasoning capabilities of large language models (LLMs). However, reinforcement learni…
Beyond the Academic Monoculture: A Unified Framework and Industrial Perspective for Attributed Graph Clustering
Yunhui Liu, Yue Liu, Yongchao Liu +4
Attributed Graph Clustering (AGC) is a fundamental unsupervised task that partitions nodes into cohesive groups by jointly modeling structural topology and node attributes. While t…
Learning Hierarchical Knowledge in Text-Rich Networks with Taxonomy-Informed Representation Learning
Yunhui Liu, Yongchao Liu, Yinfeng Chen +3
Hierarchical knowledge structures are ubiquitous across real-world domains and play a vital role in organizing information from coarse to fine semantic levels. While such structure…
Mitigating Homophily Disparity in Graph Anomaly Detection: A Scalable and Adaptive Approach
Yunhui Liu, Qizhuo Xie, Yinfeng Chen +4
Graph anomaly detection (GAD) aims to identify nodes that deviate from normal patterns in structure or features. While recent GNN-based approaches have advanced this task, they str…
Bridging Academia and Industry: A Comprehensive Benchmark for Attributed Graph Clustering
Yunhui Liu, Pengyu Qiu, Yu Xing +6
Attributed Graph Clustering (AGC) is a fundamental unsupervised task that integrates structural topology and node attributes to uncover latent patterns in graph-structured data. De…
Tabular Foundation Models are Strong Graph Anomaly Detectors
Yunhui Liu, Tieke He, Yongchao Liu +3
Graph anomaly detection (GAD), which aims to identify abnormal nodes that deviate from the majority, has become increasingly important in high-stakes Web domains. However, existing…
Towards Anomaly-Aware Pre-Training and Fine-Tuning for Graph Anomaly Detection
Yunhui Liu, Jiashun Cheng, Yiqing Lin +7
Graph anomaly detection (GAD) has garnered increasing attention in recent years, yet remains challenging due to two key factors: (1) label scarcity stemming from the high cost of a…
Norm Augmented Graph AutoEncoders for Link Prediction
Yunhui Liu, Huaisong Zhang, Xinyi Gao +3
Link Prediction (LP) is a crucial problem in graph-structured data. Graph Neural Networks (GNNs) have gained prominence in LP, with Graph AutoEncoders (GAEs) being a notable repres…
Learning Accurate, Efficient, and Interpretable MLPs on Multiplex Graphs via Node-wise Multi-View Ensemble Distillation
Yunhui Liu, Zhen Tao, Xiang Zhao +3
Multiplex graphs, with multiple edge types (graph views) among common nodes, provide richer structural semantics and better modeling capabilities. Multiplex Graph Neural Networks (…
Multi-Scale Heterogeneous Text-Attributed Graph Datasets From Diverse Domains
Yunhui Liu, Qizhuo Xie, Jinwei Shi +2
Heterogeneous Text-Attributed Graphs (HTAGs), where different types of entities are not only associated with texts but also connected by diverse relationships, have gained widespre…
Negative-Free Self-Supervised Gaussian Embedding of Graphs
Yunhui Liu, Tieke He, Tao Zheng +1
Graph Contrastive Learning (GCL) has recently emerged as a promising graph self-supervised learning framework for learning discriminative node representations without labels. The w…
Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference
Yunhui Liu, Xinyi Gao, Tieke He +2
Heterogeneous Graph Neural Networks (HGNNs) have achieved promising results in various heterogeneous graph learning tasks, owing to their superiority in capturing the intricate rel…
Scalable and Adaptive Spectral Embedding for Attributed Graph Clustering
Yunhui Liu, Tieke He, Qing Wu +2
Attributed graph clustering, which aims to group the nodes of an attributed graph into disjoint clusters, has made promising advancements in recent years. However, most existing me…
Bootstrap Latents of Nodes and Neighbors for Graph Self-Supervised Learning
Yunhui Liu, Huaisong Zhang, Tieke He +2
Contrastive learning is a significant paradigm in graph self-supervised learning. However, it requires negative samples to prevent model collapse and learn discriminative represent…
Reliable Node Similarity Matrix Guided Contrastive Graph Clustering
Yunhui Liu, Xinyi Gao, Tieke He +3
Graph clustering, which involves the partitioning of nodes within a graph into disjoint clusters, holds significant importance for numerous subsequent applications. Recently, contr…