Copertina

Graph Classification with GNNs: Optimisation, Representation and Inductive Bias

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	a, P. Krishna Kumar, Ramaswamy, Harish G.
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Commute-Time-Optimised Graphs for GNNs
di: Sterner, Igor, et al.
Pubblicazione: (2024)

Impact of Label Noise on Learning Complex Features
di: Vashisht, Rahul, et al.
Pubblicazione: (2024)

Inductive Knowledge Graph Completion with GNNs and Rules: An Analysis
di: Anil, Akash, et al.
Pubblicazione: (2023)

Optimizing Shortfall Risk Metric for Learning Regression Models
di: Ramaswamy, Harish G., et al.
Pubblicazione: (2025)

Unsupervised Optimisation of GNNs for Node Clustering
di: Leeney, William, et al.
Pubblicazione: (2024)

Node Classification and Search on the Rubik's Cube Graph with GNNs
di: Barro, Alessandro
Pubblicazione: (2025)

Rethinking the Effectiveness of Graph Classification Datasets in Benchmarks for Assessing GNNs
di: Li, Zhengdao, et al.
Pubblicazione: (2024)

Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification
di: Luo, Yuankai, et al.
Pubblicazione: (2024)

DeGLIF for Label Noise Robust Node Classification using GNNs
di: Kumar, Pintu, et al.
Pubblicazione: (2025)

Inductive Gradient Adjustment For Spectral Bias In Implicit Neural Representations
di: Shi, Kexuan, et al.
Pubblicazione: (2024)

Training the Untrainable: Introducing Inductive Bias via Representational Alignment
di: Subramaniam, Vighnesh, et al.
Pubblicazione: (2024)

Dataset Difficulty and the Role of Inductive Bias
di: Kwok, Devin, et al.
Pubblicazione: (2024)

Interpolated-MLPs: Controllable Inductive Bias
di: Wu, Sean, et al.
Pubblicazione: (2024)

Towards Exact Computation of Inductive Bias
di: Boopathy, Akhilan, et al.
Pubblicazione: (2024)

Inductive Graph Representation Learning with Quantum Graph Neural Networks
di: Faria, Arthur M., et al.
Pubblicazione: (2025)

On the Scalability of GNNs for Molecular Graphs
di: Sypetkowski, Maciej, et al.
Pubblicazione: (2024)

Theoretical Investigation on Inductive Bias of Isolation Forest
di: Zheng, Qin-Cheng, et al.
Pubblicazione: (2025)

On the Inductive Bias of Stacking Towards Improving Reasoning
di: Saunshi, Nikunj, et al.
Pubblicazione: (2024)

Understanding GNNs and Homophily in Dynamic Node Classification
di: Ito, Michael, et al.
Pubblicazione: (2025)

Locality-Aware Graph-Rewiring in GNNs
di: Barbero, Federico, et al.
Pubblicazione: (2023)

GraphViz2Vec: A Structure-aware Feature Generation Model to Improve Classification in GNNs
di: Chatterjee, Shraban Kumar, et al.
Pubblicazione: (2024)

Can TabPFN Compete with GNNs for Node Classification via Graph Tabularization?
di: Choi, Jeongwhan, et al.
Pubblicazione: (2025)

PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification
di: Tang, Huayi, et al.
Pubblicazione: (2025)

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

Data Distributional Properties As Inductive Bias for Systematic Generalization
di: del Rio, Felipe, et al.
Pubblicazione: (2025)

Explaining Grokking in Transformers through the Lens of Inductive Bias
di: Singh, Jaisidh, et al.
Pubblicazione: (2026)

MuGSI: Distilling GNNs with Multi-Granularity Structural Information for Graph Classification
di: Yao, Tianjun, et al.
Pubblicazione: (2024)

Text2Graph: Combining Lightweight LLMs and GNNs for Efficient Text Classification in Label-Scarce Scenarios
di: Sarcinelli, João Lucas Luz Lima, et al.
Pubblicazione: (2025)

VQGraph: Rethinking Graph Representation Space for Bridging GNNs and MLPs
di: Yang, Ling, et al.
Pubblicazione: (2023)

Variational Graph Auto-Encoder Based Inductive Learning Method for Semi-Supervised Classification
di: Yang, Hanxuan, et al.
Pubblicazione: (2024)

On the Role of Inductive Bias in Time-Series Pretraining: A Case Study in Learning Generalizable Representations for Clinical Time Series
di: Dey, Sharmita, et al.
Pubblicazione: (2026)

Rethinking Inductive Bias in Geographically Neural Network Weighted Regression
di: Chen, Zhenyuan
Pubblicazione: (2025)

Structural Disentanglement in Bilinear MLPs via Architectural Inductive Bias
di: Nema, Ojasva, et al.
Pubblicazione: (2026)

Structure-Guided Input Graph for GNNs facing Heterophily
di: Tenorio, Victor M., et al.
Pubblicazione: (2024)

The Road to Learning Explainable Inverse Kinematic Models: Graph Neural Networks as Inductive Bias for Symbolic Regression
di: Pandey, Pravin, et al.
Pubblicazione: (2025)

What Do GNNs Actually Learn? Towards Understanding their Representations
di: Nikolentzos, Giannis, et al.
Pubblicazione: (2023)

Half-Space Feature Learning in Neural Networks
di: Yadav, Mahesh Lorik, et al.
Pubblicazione: (2024)

A Representation-Consistent Gated Recurrent Framework for Robust Medical Time-Series Classification
di: Sai, Maitri Krishna
Pubblicazione: (2026)

Subgraphormer: Unifying Subgraph GNNs and Graph Transformers via Graph Products
di: Bar-Shalom, Guy, et al.
Pubblicazione: (2024)

Chaining 2-FWL GNNs for Combinatorial Graph Alignment
di: Lelarge, Marc
Pubblicazione: (2025)