:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Wang, Haoyu, Huang, Yinan, Wu, Nan, Li, Pan
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2410.06460
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

What Are Good Positional Encodings for Directed Graphs?
by: Huang, Yinan, et al.
Published: (2024)

What Can We Learn from State Space Models for Machine Learning on Graphs?
by: Huang, Yinan, et al.
Published: (2024)

Automated Graph Machine Learning: Approaches, Libraries, Benchmarks and Directions
by: Wang, Xin, et al.
Published: (2022)

Towards Poisoning Fair Representations
by: Liu, Tianci, et al.
Published: (2023)

Model Generalization on Text Attribute Graphs: Principles with Large Language Models
by: Wang, Haoyu, et al.
Published: (2025)

Spectral-Aware Augmentation for Enhanced Graph Representation Learning
by: Yang, Kaiqi, et al.
Published: (2023)

Spatial-temporal Graph Convolutional Networks with Diversified Transformation for Dynamic Graph Representation Learning
by: Wang, Ling, et al.
Published: (2024)

Scalable and Efficient Temporal Graph Representation Learning via Forward Recent Sampling
by: Luo, Yuhong, et al.
Published: (2024)

On the Stability of Expressive Positional Encodings for Graphs
by: Huang, Yinan, et al.
Published: (2023)

Differentially Private Relational Learning with Entity-level Privacy Guarantees
by: Huang, Yinan, et al.
Published: (2025)

Balancing Interference and Correlation in Spatial Experimental Designs: A Causal Graph Cut Approach
by: Zhu, Jin, et al.
Published: (2025)

Efficient Active Learning Halfspaces with Tsybakov Noise: A Non-convex Optimization Approach
by: Li, Yinan, et al.
Published: (2023)

A Closer Look at the Application of Causal Inference in Graph Representation Learning
by: Gao, Hang, et al.
Published: (2026)

Accelerated Sequential Flow Matching: A Bayesian Filtering Perspective
by: Huang, Yinan, et al.
Published: (2026)

Is Distance Matrix Enough for Geometric Deep Learning?
by: Li, Zian, et al.
Published: (2023)

Few-Shot Causal Representation Learning for Out-of-Distribution Generalization on Heterogeneous Graphs
by: Ding, Pengfei, et al.
Published: (2024)

OCGEC: One-class Graph Embedding Classification for DNN Backdoor Detection
by: Jiang, Haoyu, et al.
Published: (2023)

Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions
by: Li, Fang, et al.
Published: (2023)

On the Safety of Graph Representation Learning
by: Guo, Xiaoguang, et al.
Published: (2026)

Semantic-Aware Representation of Multi-Modal Data for Data Ingress: A Literature Review
by: Lamart, Pierre, et al.
Published: (2024)

Graph-KV: Breaking Sequence via Injecting Structural Biases into Large Language Models
by: Wang, Haoyu, et al.
Published: (2025)

Towards A Universal Graph Structural Encoder
by: Chen, Jialin, et al.
Published: (2025)

Hybrid Graph: A Unified Graph Representation with Datasets and Benchmarks for Complex Graphs
by: Li, Zehui, et al.
Published: (2023)

Knowledge Probing for Graph Representation Learning
by: Zhao, Mingyu, et al.
Published: (2024)

AIRCHITECT v2: Learning the Hardware Accelerator Design Space through Unified Representations
by: Seo, Jamin, et al.
Published: (2025)

Towards Stable, Globally Expressive Graph Representations with Laplacian Eigenvectors
by: Zhou, Junru, et al.
Published: (2024)

Representation Learning on Heterophilic Graph with Directional Neighborhood Attention
by: Lu, Qincheng, et al.
Published: (2024)

Jointly Learning Representations for Map Entities via Heterogeneous Graph Contrastive Learning
by: Jiang, Jiawei, et al.
Published: (2024)

Learning to Compare Hardware Designs for High-Level Synthesis
by: Bai, Yunsheng, et al.
Published: (2024)

T-GINEE: A Tensor-Based Multilayer Graph Representation Learning
by: Wang, Maolin, et al.
Published: (2026)

Benchmarking Graph Representations and Graph Neural Networks for Multivariate Time Series Classification
by: Yang, Wennuo, et al.
Published: (2025)

Node Identifiers: Compact, Discrete Representations for Efficient Graph Learning
by: Luo, Yuankai, et al.
Published: (2024)

Langevin Unlearning: A New Perspective of Noisy Gradient Descent for Machine Unlearning
by: Chien, Eli, et al.
Published: (2024)

OmniSage: Large Scale, Multi-Entity Heterogeneous Graph Representation Learning
by: Badrinath, Anirudhan, et al.
Published: (2025)

Graph Representation Learning in Biomedicine
by: Li, Michelle M., et al.
Published: (2021)

Quantum Graph Attention Network: A Novel Quantum Multi-Head Attention Mechanism for Graph Learning
by: Ning, An, et al.
Published: (2025)

FedGraph: A Research Library and Benchmark for Federated Graph Learning
by: Yao, Yuhang, et al.
Published: (2024)

Introducing Diminutive Causal Structure into Graph Representation Learning
by: Gao, Hang, et al.
Published: (2024)

Design of Experiment for Discovering Directed Mixed Graph
by: Xu, Haijie, et al.
Published: (2025)

CUDAHercules: Benchmarking Hardware-Aware Expert-level CUDA Optimization for LLMs
by: Li, Shiyang, et al.
Published: (2026)