arXiv Machine Learning

Polarity-Asymmetric Structural Calibration for Link Sign Prediction

arXiv Machine Learning

Jun 16

A polarity-aware multi-relational model for the signed interaction prediction in biological networks

arXiv:2407. 07357v3 Announce Type: replace Abstract: Predicting signed interactions in biological networks is crucial for understanding drug mechanisms and facilitating drug repurposing.

By Ziye Zhou, Meijie Wang, Lun Yu

arXiv Machine Learning

Aug 4

Nonlinear Laplacians Improve Signed-Directed Graph Learning

arXiv:2608. 00836v1 Announce Type: new Abstract: While signed-directed graphs have been studied using linear Laplacians in the design of graph neural networks, relatively little research has focused on developing non-linear Laplacian operators for such networks.

By Ali Parviz, Yuichi Yoshida

arXiv AI

Jul 28

Sign-Symmetry Learning Rules are Robust Fine-Tuners

arXiv:2502. 05925v2 Announce Type: replace-cross Abstract: Backpropagation (BP) has long been the predominant method for training neural networks due to its effectiveness.

By Aymene Berriche, Mehdi Zakaria Adjal, Riyadh Baghdadi

fine-tuning benchmarks safety

arXiv Machine Learning

Jul 31

MSGNN: A Spectral Graph Neural Network Based on a Novel Magnetic Signed Laplacian

arXiv:2209. 00546v5 Announce Type: replace-cross Abstract: Signed and directed networks are ubiquitous in real-world applications.

By Yixuan He, Michael Permultter, Gesine Reinert, Mihai Cucuringu

arXiv Machine Learning

Jun 16

Dynamic Link Prediction with Temporally Enhanced Signed Graph Neural Networks

arXiv:2605. 26290v2 Announce Type: replace Abstract: Temporal signed networks (TSNs) model the time evolution of cooperative and adversarial relationships that arise in applications such as social media analysis, trust and reputation systems, and financial transaction networks.

By Derek Regier, Andrew Polyak, Aresh Dadlani, Khosro Salmani

llms rag safety

arXiv Machine Learning

Sep 23

Signed Graph Pre-Training and Prompt Learning

arXiv:2609.25722v1 Announce Type: new Abstract: Signed graphs arise in trust--distrust networks, financial correlation systems, biological interaction graphs, and many other domains in which edges ca...

By Zihan Mei, Rong Pan, Yuzhou Chen, Yixuan He

rag fine-tuning

arXiv Machine Learning

Aug 4

HP-JEPA: Hierarchical Partitioning for Multi-Resolution Graph Joint-Embedding Predictive Learning

arXiv:2608. 00491v1 Announce Type: new Abstract: Graph self-supervised learning aims to learn transferable representations from large-scale unlabeled graph data.

By Ruichen Xu, Jingxiang Qu, Wenhan Gao, Jiaxing Zhang, Linsey Pang, Ravid Shwartz-Ziv, Yann LeCun, Yuefan Deng

rag diffusion benchmarks

arXiv Machine Learning

Jul 14

When does distribution shift break graph neural networks calibration?

arXiv:2607. 10804v1 Announce Type: new Abstract: Graph neural networks (GNNs) are increasingly deployed in real-world applications where distribution shift is un-avoidable.

By Abderaouf Bahi

benchmarks

arXiv Machine Learning

4d ago

Message Passing Does More with Less for In-Context Learning on Graphs

arXiv:2609.37057v1 Announce Type: new Abstract: Achieving strong performance with graph neural networks (GNNs) typically requires training and hyperparameter tuning for each dataset, incurring repeat...

By Dooho Lee, Jinmo Lee, Minho Jeong, Kijung Shin, Jaemin Yoo

llms

arXiv AI

Sep 16

Repurposing Unified Topological Signatures for Graph Representation Learning

arXiv:2609.17061v1 Announce Type: cross Abstract: Message-passing Graph Neural Networks (GNNs) iteratively propagate and aggregate local neighborhood information followed by global readout to learn g...

By Sanyam Sanjay Jain, Anshika Krishnatray, Aditya Sharma, Vinti Agarwal

rag benchmarks

arXiv Machine Learning

Jul 14

Surprisingly Simple and Effective Multi-Domain Graph Foundation Model through Graph-to-Table Alignment

arXiv:2607. 11374v1 Announce Type: new Abstract: Graph Foundation Models (GFMs) have emerged as a promising paradigm for learning transferable representations across diverse graph domains.

By Chunyu Hu, Tianyin Liao, Ge Lan, Xingxuan Zhang, Jianxin Li, Peng Cui, Ziwei Zhang

llms diffusion benchmarks safety

arXiv AI

Aug 10

Learning in Deep Networks under Dale's Constraint

arXiv:2608. 06963v1 Announce Type: new Abstract: Biologically plausible learning models aim to explain how neural circuits can implement effective learning under the constraints of real neurons.

By Roy Abel, Shimon Ullman

benchmarks

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