Imagen de Portada

Dynamic Graph Information Bottleneck

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Yuan, Haonan, Sun, Qingyun, Fu, Xingcheng, Ji, Cheng, Li, Jianxin
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning Artificial Intelligence
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

RAG-GFM: Overcoming In-Memory Bottlenecks in Graph Foundation Models via Retrieval-Augmented Generation
por: Yuan, Haonan, et al.
Publicado: (2026)

Robust Graph Learning Against Adversarial Evasion Attacks via Prior-Free Diffusion-Based Structure Purification
por: Luo, Jiayi, et al.
Publicado: (2025)

Decoupled and Divergence-Conditioned Prompt for Multi-domain Dynamic Graph Foundation Models
por: Yuan, Haonan, et al.
Publicado: (2026)

GraphKeeper: Graph Domain-Incremental Learning via Knowledge Disentanglement and Preservation
por: Guo, Zihao, et al.
Publicado: (2025)

Privacy Auditing of Multi-domain Graph Pre-trained Model under Membership Inference Attacks
por: Luo, Jiayi, et al.
Publicado: (2025)

GraphMoRE: Mitigating Topological Heterogeneity via Mixture of Riemannian Experts
por: Guo, Zihao, et al.
Publicado: (2024)

Discrete Curvature Graph Information Bottleneck
por: Fu, Xingcheng, et al.
Publicado: (2024)

Toward a Unified Geometry Understanding: Riemannian Diffusion Framework for Graph Generation and Prediction
por: Gao, Yisen, et al.
Publicado: (2025)

Is the Information Bottleneck Robust Enough? Towards Label-Noise Resistant Information Bottleneck Learning
por: Huang, Yi, et al.
Publicado: (2025)

Robust Graph Condensation via Classification Complexity Mitigation
por: Luo, Jiayi, et al.
Publicado: (2025)

Zero-shot Generalizable Graph Anomaly Detection with Mixture of Riemannian Experts
por: Zhao, Xinyu, et al.
Publicado: (2026)

IGL-Bench: Establishing the Comprehensive Benchmark for Imbalanced Graph Learning
por: Qin, Jiawen, et al.
Publicado: (2024)

DG-Mamba: Robust and Efficient Dynamic Graph Structure Learning with Selective State Space Models
por: Yuan, Haonan, et al.
Publicado: (2024)

SA$^{2}$GFM: Enhancing Robust Graph Foundation Models with Structure-Aware Semantic Augmentation
por: Shi, Junhua, et al.
Publicado: (2025)

GRAVER: Generative Graph Vocabularies for Robust Graph Foundation Models Fine-tuning
por: Yuan, Haonan, et al.
Publicado: (2025)

Graph Size-imbalanced Learning with Energy-guided Structural Smoothing
por: Qin, Jiawen, et al.
Publicado: (2024)

Evolving Graph Learning for Out-of-Distribution Generalization in Non-stationary Environments
por: Sun, Qingyun, et al.
Publicado: (2025)

Towards Effective, Stealthy, and Persistent Backdoor Attacks Targeting Graph Foundation Models
por: Luo, Jiayi, et al.
Publicado: (2025)

Is Fixing Schema Graphs Necessary? Full-Resolution Graph Structure Learning for Relational Deep Learning
por: Huang, Yi, et al.
Publicado: (2026)

Learning Fair Graph Representations with Multi-view Information Bottleneck
por: Liu, Chuxun, et al.
Publicado: (2025)

Bi-Directional Multi-Scale Graph Dataset Condensation via Information Bottleneck
por: Fu, Xingcheng, et al.
Publicado: (2024)

Interpretable Prototype-based Graph Information Bottleneck
por: Seo, Sangwoo, et al.
Publicado: (2023)

IBNorm: Information-Bottleneck Inspired Normalization for Representation Learning
por: Zou, Xiandong, et al.
Publicado: (2025)

GaGSL: Global-augmented Graph Structure Learning via Graph Information Bottleneck
por: Li, Shuangjie, et al.
Publicado: (2024)

Graph Structure Learning with Temporal Graph Information Bottleneck for Inductive Representation Learning
por: Xiong, Jiafeng, et al.
Publicado: (2025)

Discovering the Representation Bottleneck of Graph Neural Networks
por: Wu, Fang, et al.
Publicado: (2022)

Eliminating Information Leakage in Hard Concept Bottleneck Models with Supervised, Hierarchical Concept Learning
por: Sun, Ao, et al.
Publicado: (2024)

Invariant Graph Learning Meets Information Bottleneck for Out-of-Distribution Generalization
por: Mao, Wenyu, et al.
Publicado: (2024)

TimeSieve: Extracting Temporal Dynamics through Information Bottlenecks
por: Feng, Ninghui, et al.
Publicado: (2024)

Exploring Dynamic Properties of Backdoor Training Through Information Bottleneck
por: Liu, Xinyu, et al.
Publicado: (2025)

Revealing Combinatorial Reasoning of GNNs via Graph Concept Bottleneck Layer
por: Niu, Yue, et al.
Publicado: (2026)

Pre-training Graph Neural Networks on Molecules by Using Subgraph-Conditioned Graph Information Bottleneck
por: Hoang, Van Thuy, et al.
Publicado: (2024)

Contrastive Graph Representation Learning with Adversarial Cross-view Reconstruction and Information Bottleneck
por: Shou, Yuntao, et al.
Publicado: (2024)

MLGIB: Multi-Label Graph Information Bottleneck for Expressive and Robust Message Passing
por: Wu, Chaokai, et al.
Publicado: (2026)

Hyperbolic Geometric Latent Diffusion Model for Graph Generation
por: Fu, Xingcheng, et al.
Publicado: (2024)

GC-Bench: An Open and Unified Benchmark for Graph Condensation
por: Sun, Qingyun, et al.
Publicado: (2024)

Poincaré Differential Privacy for Hierarchy-Aware Graph Embedding
por: Wei, Yuecen, et al.
Publicado: (2023)

Revisiting Counterfactual Regression through the Lens of Gromov-Wasserstein Information Bottleneck
por: Yang, Hao, et al.
Publicado: (2024)

AgenticGEO: A Self-Evolving Agentic System for Generative Engine Optimization
por: Yuan, Jiaqi, et al.
Publicado: (2026)

Federated Graph Condensation with Information Bottleneck Principles
por: Yan, Bo, et al.
Publicado: (2024)