Copertina

State Space Models on Temporal Graphs: A First-Principles Study

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Li, Jintang, Wu, Ruofan, Jin, Xinzhou, Ma, Boqun, Chen, Liang, Zheng, Zibin
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Are Large Language Models In-Context Graph Learners?
di: Li, Jintang, et al.
Pubblicazione: (2025)

Oversmoothing: A Nightmare for Graph Contrastive Learning?
di: Li, Jintang, et al.
Pubblicazione: (2023)

A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks
di: Li, Jintang, et al.
Pubblicazione: (2023)

SGHormer: An Energy-Saving Graph Transformer Driven by Spikes
di: Zhang, Huizhe, et al.
Pubblicazione: (2024)

Revisiting Graph Autoencoders as Implicit Contrastive Learners
di: Li, Jintang, et al.
Pubblicazione: (2024)

Revisiting Modularity Maximization for Graph Clustering: A Contrastive Learning Perspective
di: Liu, Yunfei, et al.
Pubblicazione: (2024)

SGNNBench: A Holistic Evaluation of Spiking Graph Neural Network on Large-scale Graph
di: Zhang, Huizhe, et al.
Pubblicazione: (2025)

Expand and Compress: Exploring Tuning Principles for Continual Spatio-Temporal Graph Forecasting
di: Chen, Wei, et al.
Pubblicazione: (2024)

DyGMamba: Efficiently Modeling Long-Term Temporal Dependency on Continuous-Time Dynamic Graphs with State Space Models
di: Ding, Zifeng, et al.
Pubblicazione: (2024)

STG-Mamba: Spatial-Temporal Graph Learning via Selective State Space Model
di: Li, Lincan, et al.
Pubblicazione: (2024)

Fair Graph Representation Learning via Sensitive Attribute Disentanglement
di: Zhu, Yuchang, et al.
Pubblicazione: (2024)

Agentic-VLA: Efficient Online Adaptation for Vision-Language-Action Models
di: Jin, Ruofan, et al.
Pubblicazione: (2026)

State Space Models over Directed Graphs
di: She, Junzhi, et al.
Pubblicazione: (2025)

L^2CL: Embarrassingly Simple Layer-to-Layer Contrastive Learning for Graph Collaborative Filtering
di: Jin, Xinzhou, et al.
Pubblicazione: (2024)

Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces
di: Wang, Chloe, et al.
Pubblicazione: (2024)

One Fits All: Learning Fair Graph Neural Networks for Various Sensitive Attributes
di: Zhu, Yuchang, et al.
Pubblicazione: (2024)

SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding
di: Zhu, Yuchang, et al.
Pubblicazione: (2025)

Temporal Knowledge Graph Completion with Time-sensitive Relations in Hypercomplex Space
di: Cai, Li, et al.
Pubblicazione: (2024)

A Historical Trajectory Assisted Optimization Method for Zeroth-Order Federated Learning
di: Wu, Chenlin, et al.
Pubblicazione: (2024)

SpoT-Mamba: Learning Long-Range Dependency on Spatio-Temporal Graphs with Selective State Spaces
di: Choi, Jinhyeok, et al.
Pubblicazione: (2024)

UniMamba: A Unified Spatial-Temporal Modeling Framework with State-Space and Attention Integration
di: Chen, Xingsheng, et al.
Pubblicazione: (2026)

Hierarchical Spatio-Temporal State-Space Modeling for fMRI Analysis
di: Wei, Yuxiang, et al.
Pubblicazione: (2024)

Exploring Graph Mamba: A Comprehensive Survey on State-Space Models for Graph Learning
di: Atitallah, Safa Ben, et al.
Pubblicazione: (2024)

Temporal Generalization Estimation in Evolving Graphs
di: Lu, Bin, et al.
Pubblicazione: (2024)

GeoMAE: Masking Representation Learning for Spatio-Temporal Graph Forecasting with Missing Values
di: Ke, Songyu, et al.
Pubblicazione: (2025)

Learning Multi-Indicator Weights for Data Selection: A Joint Task-Model Adaptation Framework with Efficient Proxies
di: Song, Jingze, et al.
Pubblicazione: (2026)

Efficient State Space Model via Fast Tensor Convolution and Block Diagonalization
di: Liang, Tongyi, et al.
Pubblicazione: (2024)

GRExplainer: A Universal Explanation Method for Temporal Graph Neural Networks
di: Li, Xuyan, et al.
Pubblicazione: (2025)

MODULI: Unlocking Preference Generalization via Diffusion Models for Offline Multi-Objective Reinforcement Learning
di: Yuan, Yifu, et al.
Pubblicazione: (2024)

GLADMamba: Unsupervised Graph-Level Anomaly Detection Powered by Selective State Space Model
di: Fu, Yali, et al.
Pubblicazione: (2025)

Towards Expressive Spectral-Temporal Graph Neural Networks for Time Series Forecasting
di: Jin, Ming, et al.
Pubblicazione: (2023)

UrbanFM: Scaling Urban Spatio-Temporal Foundation Models
di: Chen, Wei, et al.
Pubblicazione: (2026)

Robust Unsupervised Fault Diagnosis For High-Dimensional Nonlinear Noisy Data
di: Zhao, Dandan, et al.
Pubblicazione: (2025)

Exploring Adversarial Robustness of Deep State Space Models
di: Qi, Biqing, et al.
Pubblicazione: (2024)

Rethinking Efficient Graph Coarsening via a Non-Selfishness Principle
di: Bai, Xu, et al.
Pubblicazione: (2026)

Temporal-Aware Evaluation and Learning for Temporal Graph Neural Networks
di: Su, Junwei, et al.
Pubblicazione: (2024)

Disentangling Multiplex Spatial-Temporal Transition Graph Representation Learning for Socially Enhanced POI Recommendation
di: Li, Jie, et al.
Pubblicazione: (2025)

Mitigating the Structural Bias in Graph Adversarial Defenses
di: Fang, Junyuan, et al.
Pubblicazione: (2025)

Spatial-Temporal Mixture-of-Graph-Experts for Multi-Type Crime Prediction
di: Wu, Ziyang, et al.
Pubblicazione: (2024)

From Semantics to Hierarchy: A Hybrid Euclidean-Tangent-Hyperbolic Space Model for Temporal Knowledge Graph Reasoning
di: Feng, Siling, et al.
Pubblicazione: (2024)