Cover Image

On the Scalability of GNNs for Molecular Graphs

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Sypetkowski, Maciej, Wenkel, Frederik, Poursafaei, Farimah, Dickson, Nia, Suri, Karush, Fradkin, Philip, Beaini, Dominique
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

How Molecules Impact Cells: Unlocking Contrastive PhenoMolecular Retrieval
by: Fradkin, Philip, et al.
Published: (2024)

SeedER: Seed-and-Expand Retrieval from Knowledge Graphs
by: Shirzad, Hamed, et al.
Published: (2026)

Temporal Graph Rewiring with Expander Graphs
by: Petrović, Katarina, et al.
Published: (2024)

Temporal Graph Analysis with TGX
by: Shirzadkhani, Razieh, et al.
Published: (2024)

UTG: Towards a Unified View of Snapshot and Event Based Models for Temporal Graphs
by: Huang, Shenyang, et al.
Published: (2024)

Towards a General Recipe for Combinatorial Optimization with Multi-Filter GNNs
by: Wenkel, Frederik, et al.
Published: (2024)

Overcoming Oversmoothness in Graph Convolutional Networks via Hybrid Scattering Networks
by: Wenkel, Frederik, et al.
Published: (2022)

ET-Flow: Equivariant Flow-Matching for Molecular Conformer Generation
by: Hassan, Majdi, et al.
Published: (2024)

Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology
by: Kraus, Oren, et al.
Published: (2024)

MiNT: Multi-Network Training for Transfer Learning on Temporal Graphs
by: Shamsi, Kiarash, et al.
Published: (2024)

Graph Positional and Structural Encoder
by: Cantürk, Semih, et al.
Published: (2023)

TGM: a Modular and Efficient Library for Machine Learning on Temporal Graphs
by: Chmura, Jacob, et al.
Published: (2025)

Self-Refining Training for Amortized Density Functional Theory
by: Hassan, Majdi, et al.
Published: (2025)

TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs
by: Gastinger, Julia, et al.
Published: (2024)

Direct Molecular Polarizability Prediction with SO(3) Equivariant Local Frame GNNs
by: Filling, Jean Philip, et al.
Published: (2025)

CoRe-GD: A Hierarchical Framework for Scalable Graph Visualization with GNNs
by: Grötschla, Florian, et al.
Published: (2024)

Expressivity and Generalization: Fragment-Biases for Molecular GNNs
by: Wollschläger, Tom, et al.
Published: (2024)

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

A Cross Modal Knowledge Distillation & Data Augmentation Recipe for Improving Transcriptomics Representations through Morphological Features
by: Bendidi, Ihab, et al.
Published: (2025)

Can Computational Reducibility Lead to Transferable Models for Graph Combinatorial Optimization?
by: Cantürk, Semih, et al.
Published: (2026)

Improving Molecular Modeling with Geometric GNNs: an Empirical Study
by: Ramlaoui, Ali, et al.
Published: (2024)

Message Passing on the Edge: Towards Scalable and Expressive GNNs
by: Barceló, Pablo, et al.
Published: (2025)

Towards Autonomous Mechanistic Reasoning in Virtual Cells
by: Jang, Yunhui, et al.
Published: (2026)

Self-Exploring Language Models for Explainable Link Forecasting on Temporal Graphs via Reinforcement Learning
by: Ding, Zifeng, et al.
Published: (2025)

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

Graph Classification with GNNs: Optimisation, Representation and Inductive Bias
by: a, P. Krishna Kumar, et al.
Published: (2024)

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

Flow Matters: Directional and Expressive GNNs for Heterophilic Graphs
by: Gupta, Arman, et al.
Published: (2025)

Rethinking the Expressive Power of GNNs via Graph Biconnectivity
by: Zhang, Bohang, et al.
Published: (2023)

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

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

Classifying Nodes in Graphs without GNNs
by: Winter, Daniel, et al.
Published: (2024)

Benchmarking Positional Encodings for GNNs and Graph Transformers
by: Grötschla, Florian, et al.
Published: (2024)

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

A Signed Graph Approach to Understanding and Mitigating Oversmoothing in GNNs
by: Wang, Jiaqi, et al.
Published: (2025)

Empowering GNNs for Domain Adaptation via Denoising Target Graph
by: Yu, Haiyang, et al.
Published: (2025)

Graph Learning at Scale: Characterizing and Optimizing Pre-Propagation GNNs
by: Yue, Zichao, et al.
Published: (2025)

Solving Oversmoothing in GNNs via Nonlocal Message Passing: Algebraic Smoothing and Depth Scalability
by: Guan, Weiqi, et al.
Published: (2025)

Advancing Heatwave Forecasting via Distribution Informed-Graph Neural Networks (DI-GNNs): Integrating Extreme Value Theory with GNNs
by: Chishtie, Farrukh A., et al.
Published: (2024)

PowerGNN: A Topology-Aware Graph Neural Network for Electricity Grids
by: Suri, Dhruv, et al.
Published: (2025)