Cover Image

Spatial-Temporal Mixture-of-Graph-Experts for Multi-Type Crime Prediction

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Wu, Ziyang, Liu, Fan, Han, Jindong, Liang, Yuxuan, Liu, Hao
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

FaST: Efficient and Effective Long-Horizon Forecasting for Large-Scale Spatial-Temporal Graphs via Mixture-of-Experts
by: Zhao, Yiji, et al.
Published: (2026)

Meta-Transfer Learning Powered Temporal Graph Networks for Cross-City Real Estate Appraisal
by: Zhang, Weijia, et al.
Published: (2024)

Uncertainty-Aware Crime Prediction With Spatial Temporal Multivariate Graph Neural Networks
by: Wang, Zepu, et al.
Published: (2024)

Unsupervised Graph Anomaly Detection via Multi-Hypersphere Heterophilic Graph Learning
by: Ni, Hang, et al.
Published: (2025)

TacticExpert: Spatial-Temporal Graph Language Model for Basketball Tactics
by: Lingrui, Xu, et al.
Published: (2025)

DAMBench: A Multi-Modal Benchmark for Deep Learning-based Atmospheric Data Assimilation
by: Wang, Hao, et al.
Published: (2025)

RePST: Language Model Empowered Spatio-Temporal Forecasting via Semantic-Oriented Reprogramming
by: Wang, Hao, et al.
Published: (2024)

Efficiently Editing Mixture-of-Experts Models with Compressed Experts
by: He, Yifei, et al.
Published: (2025)

HiMoE: Heterogeneity-Informed Mixture-of-Experts for Fair Spatial-Temporal Forecasting
by: Yu, Shaohan, et al.
Published: (2024)

GraphLoRA: Structure-Aware Contrastive Low-Rank Adaptation for Cross-Graph Transfer Learning
by: Yang, Zhe-Rui, et al.
Published: (2024)

Irregular Traffic Time Series Forecasting Based on Asynchronous Spatio-Temporal Graph Convolutional Network
by: Zhang, Weijia, et al.
Published: (2023)

ST-MoE-BERT: A Spatial-Temporal Mixture-of-Experts Framework for Long-Term Cross-City Mobility Prediction
by: He, Haoyu, et al.
Published: (2024)

PhyDA: Physics-Guided Diffusion Models for Data Assimilation in Atmospheric Systems
by: Wang, Hao, et al.
Published: (2025)

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts
by: Li, Cheng, et al.
Published: (2025)

LightMoE: Reducing Mixture-of-Experts Redundancy through Expert Replacing
by: Hao, Jiawei, et al.
Published: (2026)

Erase then Rectify: A Training-Free Parameter Editing Approach for Cost-Effective Graph Unlearning
by: Yang, Zhe-Rui, et al.
Published: (2024)

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

Graph Knowledge Distillation to Mixture of Experts
by: Rumiantsev, Pavel, et al.
Published: (2024)

Mixture of Weak & Strong Experts on Graphs
by: Zeng, Hanqing, et al.
Published: (2023)

Multi-Head Mixture-of-Experts
by: Wu, Xun, et al.
Published: (2024)

Multi-Modal Time Series Prediction via Mixture of Modulated Experts
by: Zhang, Lige, et al.
Published: (2026)

MC#: Mixture Compressor for Mixture-of-Experts Large Models
by: Huang, Wei, et al.
Published: (2025)

Klotski: Efficient Mixture-of-Expert Inference via Expert-Aware Multi-Batch Pipeline
by: Fang, Zhiyuan, et al.
Published: (2025)

Routing-Free Mixture-of-Experts
by: Liu, Yilun, et al.
Published: (2026)

Damba-ST: Domain-Adaptive Mamba for Efficient Urban Spatio-Temporal Prediction
by: An, Rui, et al.
Published: (2025)

DA-MoE: Addressing Depth-Sensitivity in Graph-Level Analysis through Mixture of Experts
by: Yao, Zelin, et al.
Published: (2024)

Modeling Expert Interactions in Sparse Mixture of Experts via Graph Structures
by: Nguyen-Nhat, Minh-Khoi, et al.
Published: (2025)

Mixture of Diverse Size Experts
by: Sun, Manxi, et al.
Published: (2024)

On the Spatial Structure of Mixture-of-Experts in Transformers
by: Bershatsky, Daniel, et al.
Published: (2025)

MoEMeta: Mixture-of-Experts Meta Learning for Few-Shot Relational Learning
by: Wu, Han, et al.
Published: (2025)

Accelerating Mixture-of-Expert Inference with Adaptive Expert Split Mechanism
by: Yan, Jiaming, et al.
Published: (2025)

Mixture-of-Experts Meets In-Context Reinforcement Learning
by: Wu, Wenhao, et al.
Published: (2025)

UniPool: A Globally Shared Expert Pool for Mixture-of-Experts
by: Huang, Minbin, et al.
Published: (2026)

HyperMoE: Towards Better Mixture of Experts via Transferring Among Experts
by: Zhao, Hao, et al.
Published: (2024)

Global-Aware Enhanced Spatial-Temporal Graph Recurrent Networks: A New Framework For Traffic Flow Prediction
by: Liu, Haiyang, et al.
Published: (2024)

Dynamic Experts Search: Enhancing Reasoning in Mixture-of-Experts LLMs at Test Time
by: Han, Yixuan, et al.
Published: (2025)

Mixture of Experts based Multi-task Supervise Learning from Crowds
by: Han, Tao, et al.
Published: (2024)

AnyExperts: On-Demand Expert Allocation for Multimodal Language Models with Mixture of Expert
by: Gao, Yuting, et al.
Published: (2025)

Graph-Conditioned Mixture of Graph Neural Network Experts for Traffic Forecasting
by: Ghaffari, Amirhossein, et al.
Published: (2026)

STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting
by: Liang, Zhuding, et al.
Published: (2025)