:: Library Catalog

Copertina

Salvato in:

Dettagli Bibliografici
Autori principali:	Wang, Jingjing, Zhu, Hongjie, Xie, Haoran, Wang, Fu Lee, Xu, Xiaoliang, Wang, Yuxiang
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning Artificial Intelligence
Accesso online:	https://arxiv.org/abs/2412.12185
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

GED-Consistent Disentanglement of Aligned and Unaligned Substructures for Graph Similarity Learning
di: Zhan, Zhentao, et al.
Pubblicazione: (2025)

Explaining Graph Neural Networks for Node Similarity on Graphs
di: Daza, Daniel, et al.
Pubblicazione: (2024)

Graph Unlearning: Efficient Node Removal in Graph Neural Networks
di: Guan, Faqian, et al.
Pubblicazione: (2025)

Preserving Node Distinctness in Graph Autoencoders via Similarity Distillation
di: Chen, Ge, et al.
Pubblicazione: (2024)

BuffGraph: Enhancing Class-Imbalanced Node Classification via Buffer Nodes
di: Wang, Qian, et al.
Pubblicazione: (2024)

ENADPool: The Edge-Node Attention-based Differentiable Pooling for Graph Neural Networks
di: Zhao, Zhehan, et al.
Pubblicazione: (2024)

Faithful Interpretation for Graph Neural Networks
di: Hu, Lijie, et al.
Pubblicazione: (2024)

Characterizing the Influence of Topology on Graph Learning Tasks
di: Wu, Kailong, et al.
Pubblicazione: (2024)

SCL-GNN: Towards Generalizable Graph Neural Networks via Spurious Correlation Learning
di: Zhang, Yuxiang, et al.
Pubblicazione: (2026)

Every Node is Different: Dynamically Fusing Self-Supervised Tasks for Attributed Graph Clustering
di: Zhu, Pengfei, et al.
Pubblicazione: (2024)

Neural Dynamics-Informed Pre-trained Framework for Personalized Brain Functional Network Construction
di: Jiang, Hongjie, et al.
Pubblicazione: (2026)

Efficient and Scalable Granular-ball Graph Coarsening Method for Large-scale Graph Node Classification
di: Wang, Guan, et al.
Pubblicazione: (2026)

SIG: Efficient Self-Interpretable Graph Neural Network for Continuous-time Dynamic Graphs
di: Fang, Lanting, et al.
Pubblicazione: (2024)

Rethinking Flexible Graph Similarity Computation: One-step Alignment with Global Guidance
di: Liu, Zhouyang, et al.
Pubblicazione: (2025)

One Node Per User: Node-Level Federated Learning for Graph Neural Networks
di: Gao, Zhidong, et al.
Pubblicazione: (2024)

Learning to Detect Critical Nodes in Sparse Graphs via Feature Importance Awareness
di: Tan, Xuwei, et al.
Pubblicazione: (2021)

GARNN: An Interpretable Graph Attentive Recurrent Neural Network for Predicting Blood Glucose Levels via Multivariate Time Series
di: Piao, Chengzhe, et al.
Pubblicazione: (2024)

Unveiling Global Interactive Patterns across Graphs: Towards Interpretable Graph Neural Networks
di: Wang, Yuwen, et al.
Pubblicazione: (2024)

Node Level Graph Autoencoder: Unified Pretraining for Textual Graph Learning
di: Hu, Wenbin, et al.
Pubblicazione: (2024)

On the Utilization of Unique Node Identifiers in Graph Neural Networks
di: Bechler-Speicher, Maya, et al.
Pubblicazione: (2024)

Parallelizing Node-Level Explainability in Graph Neural Networks
di: Llorente, Oscar, et al.
Pubblicazione: (2026)

TinyGraph: Joint Feature and Node Condensation for Graph Neural Networks
di: Liu, Yezi, et al.
Pubblicazione: (2024)

Space Alignment Matters: The Missing Piece for Inducing Neural Collapse in Long-Tailed Learning
di: Wang, Jinping, et al.
Pubblicazione: (2025)

Delayed Bottlenecking: Alleviating Forgetting in Pre-trained Graph Neural Networks
di: Zhao, Zhe, et al.
Pubblicazione: (2024)

Rethinking Graph Contrastive Learning through Relative Similarity Preservation
di: Ning, Zhiyuan, et al.
Pubblicazione: (2025)

Fast and Interpretable Protein Substructure Alignment via Optimal Transport
di: Wang, Zhiyu, et al.
Pubblicazione: (2025)

Interpretable Air Pollution Forecasting by Physics-Guided Spatiotemporal Decoupling
di: Zhang, Zhiguo, et al.
Pubblicazione: (2025)

Measuring Time-Series Dataset Similarity using Wasserstein Distance
di: Chen, Hongjie, et al.
Pubblicazione: (2025)

Weighted Graph Structure Learning with Attention Denoising for Node Classification
di: Wang, Tingting, et al.
Pubblicazione: (2025)

Graph Neural Network Causal Explanation via Neural Causal Models
di: Behnam, Arman, et al.
Pubblicazione: (2024)

Enhancing Fairness in Autoencoders for Node-Level Graph Anomaly Detection
di: Wang, Shouju, et al.
Pubblicazione: (2025)

Deep Graph Neural Networks via Posteriori-Sampling-based Node-Adaptive Residual Module
di: Zhou, Jingbo, et al.
Pubblicazione: (2023)

GIN-SD: Source Detection in Graphs with Incomplete Nodes via Positional Encoding and Attentive Fusion
di: Cheng, Le, et al.
Pubblicazione: (2024)

DFA-GNN: Forward Learning of Graph Neural Networks by Direct Feedback Alignment
di: Zhao, Gongpei, et al.
Pubblicazione: (2024)

Interpretable Neuropsychiatric Diagnosis via Concept-Guided Graph Neural Networks
di: Wang, Song, et al.
Pubblicazione: (2025)

Fast and Distributed Equivariant Graph Neural Networks by Virtual Node Learning
di: Zhang, Yuelin, et al.
Pubblicazione: (2025)

Pure Node Selection for Imbalanced Graph Node Classification
di: Zeng, Fanlong, et al.
Pubblicazione: (2025)

Interpretable Safety Alignment via SAE-Constructed Low-Rank Subspace Adaptation
di: Wang, Dianyun, et al.
Pubblicazione: (2025)

Fast and Robust Contextual Node Representation Learning over Dynamic Graphs
di: Guo, Xingzhi, et al.
Pubblicazione: (2024)

Poisoning the Inner Prediction Logic of Graph Neural Networks for Clean-Label Backdoor Attacks
di: Zhang, Yuxiang, et al.
Pubblicazione: (2026)