:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Chu, Xu, Xue, Hanlin, Tan, Zhijie, Wang, Bingce, Mo, Tong, Li, Weiping
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2501.14427
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning
by: Chu, Xu, et al.
Published: (2025)

Domaino1s: Guiding LLM Reasoning for Explainable Answers in High-Stakes Domains
by: Chu, Xu, et al.
Published: (2025)

Towards Order Fairness: Mitigating LLMs Order Sensitivity through Dual Group Advantage Optimization
by: Chu, Xu, et al.
Published: (2026)

Order Matters: Exploring Order Sensitivity in Multimodal Large Language Models
by: Tan, Zhijie, et al.
Published: (2024)

Mitigating Hallucinations on Object Attributes using Multiview Images and Negative Instructions
by: Tan, Zhijie, et al.
Published: (2025)

Qwen Look Again: Guiding Vision-Language Reasoning Models to Re-attention Visual Information
by: Chu, Xu, et al.
Published: (2025)

How to Use Graph Data in the Wild to Help Graph Anomaly Detection?
by: Cao, Yuxuan, et al.
Published: (2025)

What Do LLMs Need to Understand Graphs: A Survey of Parametric Representation of Graphs
by: Fu, Dongqi, et al.
Published: (2024)

Similarity-based Neighbor Selection for Graph LLMs
by: Li, Rui, et al.
Published: (2024)

When Structure Doesn't Help: LLMs Do Not Read Text-Attributed Graphs as Effectively as We Expected
by: Xu, Haotian, et al.
Published: (2025)

Kernel Alignment-based Multi-view Unsupervised Feature Selection with Sample-level Adaptive Graph Learning
by: Tan, Yalan, et al.
Published: (2026)

Graph Propagation Transformer for Graph Representation Learning
by: Chen, Zhe, et al.
Published: (2023)

Tri-Learn Graph Fusion Network for Attributed Graph Clustering
by: Li, Binxiong, et al.
Published: (2025)

The Importance of Model Inspection for Better Understanding Performance Characteristics of Graph Neural Networks
by: Shehata, Nairouz, et al.
Published: (2024)

GraphICL: Unlocking Graph Learning Potential in LLMs through Structured Prompt Design
by: Sun, Yuanfu, et al.
Published: (2025)

Graph Representation Learning Augmented Model Manipulation on Federated Fine-Tuning of LLMs
by: Cai, Hanlin, et al.
Published: (2026)

DRGW: Learning Disentangled Representations for Robust Graph Watermarking
by: Li, Jiasen, et al.
Published: (2026)

Mitigating Label Noise on Graph via Topological Sample Selection
by: Wu, Yuhao, et al.
Published: (2024)

Graph Random Walk with Feature-Label Space Alignment: A Multi-Label Feature Selection Method
by: Gao, Wanfu, et al.
Published: (2025)

Graph Data Selection for Domain Adaptation: A Model-Free Approach
by: Li, Ting-Wei, et al.
Published: (2025)

GRAFT: Graph-Tokenized LLMs for Tool Planning
by: Gao, Xinyi, et al.
Published: (2026)

Graph Condensation for Open-World Graph Learning
by: Gao, Xinyi, et al.
Published: (2024)

Understanding When Graph Convolutional Networks Help: A Diagnostic Study on Label Scarcity and Structural Properties
by: Subedi, Nischal, et al.
Published: (2025)

Graph Homophily Booster: Rethinking the Role of Discrete Features on Heterophilic Graphs
by: Qiu, Ruizhong, et al.
Published: (2025)

Double-Diffusion: ODE-Prior Accelerated Diffusion Models for Spatio-Temporal Graph Forecasting
by: Dong, Hanlin, et al.
Published: (2025)

GraphRCG: Self-Conditioned Graph Generation
by: Wang, Song, et al.
Published: (2024)

Are LLMs Better GNN Helpers? Rethinking Robust Graph Learning under Deficiencies with Iterative Refinement
by: Wang, Zhaoyan, et al.
Published: (2025)

FedGRec: Dynamic Spatio-Temporal Federated Graph Learning for Secure and Efficient Cross-Border Recommendations
by: Tan, Zhizhong, et al.
Published: (2025)

Understanding Heterophily for Graph Neural Networks
by: Wang, Junfu, et al.
Published: (2024)

SamGoG: A Sampling-Based Graph-of-Graphs Framework for Imbalanced Graph Classification
by: Wang, Shangyou, et al.
Published: (2025)

AS-GCL: Asymmetric Spectral Augmentation on Graph Contrastive Learning
by: Liu, Ruyue, et al.
Published: (2025)

Better Not to Propagate: Understanding Edge Uncertainty and Over-smoothing in Signed Graph Neural Networks
by: Choi, Yoonhyuk, et al.
Published: (2024)

Adaptive Graph Mixture of Residual Experts: Unsupervised Learning on Diverse Graphs with Heterogeneous Specialization
by: Chu, Yunlong, et al.
Published: (2025)

Efficient Graph Understanding with LLMs via Structured Context Injection
by: Waghmare, Govind, et al.
Published: (2025)

Can LLMs Convert Graphs to Text-Attributed Graphs?
by: Wang, Zehong, et al.
Published: (2024)

LLMTM: Benchmarking and Optimizing LLMs for Temporal Motif Analysis in Dynamic Graphs
by: Hao, Bing, et al.
Published: (2025)

GraphFM: A Comprehensive Benchmark for Graph Foundation Model
by: Xu, Yuhao, et al.
Published: (2024)

Better Call Graphs: A New Dataset of Function Call Graphs for Malware Classification
by: Hossain, Jakir, et al.
Published: (2025)

Applying Self-supervised Learning to Network Intrusion Detection for Network Flows with Graph Neural Network
by: Xu, Renjie, et al.
Published: (2024)

Distributed-Order Fractional Graph Operating Network
by: Zhao, Kai, et al.
Published: (2024)