arXiv Statistics ML

Graph Neural Networks for Supply Chain Management: A Framework and Real World Benchmark

arXiv Machine Learning

Jul 21

On the Potential of Graph Neural Networks as Metamodels for Supply Chain Optimization: Dataset, Architectures, and Directions

arXiv:2607. 16769v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have emerged as a powerful, differentiable class of learning models for graph-structured systems.

By Tushar Lone, Neha Karanjkar

arXiv Machine Learning

Jun 30

GLIP: Graph and LLM Joint Pretraining for Graph-Level Tasks

arXiv:2606. 29773v1 Announce Type: new Abstract: Graphs are widely used to model relational systems, with applications in domains such as social networks, finance, and biomedicine.

By Haoxin Sun, Yiqing Lin, Yajun Huang, Chenhui Dong, Mingjun Li, Zhongzhi Zhang

llms diffusion fine-tuning benchmarks safety

arXiv Machine Learning

Jul 30

Examining the Efficacy of Graph Neural Network Message-Passing in Regression Contexts

arXiv:2607. 26404v1 Announce Type: new Abstract: Graph Neural Networks (GNN) facilitate effective prediction on graph data such as molecules, media networks and neural network blueprints.

By Keith G. Mills, Aedan J. DeFrates, Joong Ho Kim

benchmarks

arXiv Machine Learning

Jun 4

Fixed Aggregation Features Can Rival GNNs

arXiv:2601. 19449v2 Announce Type: replace Abstract: Graph neural networks (GNNs) are widely believed to excel at node representation learning through trainable neighborhood aggregations.

By Celia Rubio-Madrigal, Rebekka Burkholz

llms benchmarks safety

arXiv Machine Learning

Sep 24

Learning to Approximate Uniform Facility Location via Graph Neural Networks

The paper introduces a fully differentiable message‑passing neural network (MPNN) designed to approximate the Uniform Facility Location (UniFL) problem. Unlike many learning‑based approaches that require supervision or reinforcement learning, this model incorporates principles from classical approximation algorithms, providing provable approximation guarantees. Empirical results show that it outperforms standard approximation algorithms and reduces the performance gap to integer linear programming solutions.

By Chendi Qian, Christopher Morris, Stefanie Jegelka, Christian Sohler

nlp reinforcement-learning

arXiv Machine Learning

Aug 4

Empowering Credit Risk Detection in Weixin Pay with Billion-Scale Deep Graph Learning

arXiv:2608. 02168v1 Announce Type: new Abstract: Credit risk detection, particularly mitigating individual fraud, is crucial for maintaining the stability of digital financial ecosystems.

By Xin Liu, Xiyuan Chen, Chenglong Wu, Xuan Zong, Jun Zhou, Dawei Cheng

diffusion safety

arXiv AI

Jun 29

GNBAN: Graph Neural Basis Attention Networks for Long-Horizon Forecasting over Large Entity Sets

arXiv:2606. 27863v1 Announce Type: cross Abstract: Demand forecasting at the bottom of a retail hierarchy requires predicting tens of thousands of correlated long-horizon series across products, stores, and regions.

By Janak M. Patel, Anirudh Deodhar, Dagnachew Birru

benchmarks safety

arXiv Machine Learning

Jun 2

OgBench: A Framework for Evaluating Graph Neural Networks on Omics Data

arXiv:2605. 15511v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) have become the dominant framework for inductive graph-level learning.

By Louisa Cornelis, Johan Mathe, Louis Van Langendonck, Guillermo Bern\'ardez, Nina Miolane

benchmarks

arXiv AI

Jul 20

Knowledge-Assisted Multi-Graph Dependency Learning for Multivariate Time Series Anomaly Detection in Multi-Stage Industrial Processes

arXiv:2607. 15799v1 Announce Type: cross Abstract: Industrial processes often generate complex, interdependent time-series data from multiple sensors across multiple stages, forming complex dependencies among variables and process stages.

By Jaeyeong Lee, Taeseong Yoon, Wonmo Koo, Heeyoung Kim

arXiv Machine Learning

Sep 16

Unified Heterogeneous Graph Neural Network solver for Power Flow, Optimal Power Flow and State Estimation

arXiv:2609.16738v1 Announce Type: cross Abstract: Power Flow (PF), Optimal Power Flow (OPF), and State Estimation (SE) are fundamental problems in power system analysis, but solving them is computati...

By Ferran Bohigas-Daranas, Hamid Latif-Mart\'inez, Eduardo Prieto-Araujo, Oriol Gomis-Bellmunt, Pere Barlet-Ros

arXiv Machine Learning

Jun 25

Auto-Configured Explainable Graph Neural Networks for Multi-Site Pollution Prediction

arXiv:2606. 24978v1 Announce Type: new Abstract: Accurate particulate matter (PM) prediction is crucial for mitigating air pollution.

By Abdelkader Dairi, Fouzi Harrou, Ying Sun

Hugging Face Trending Papers

Aug 3

Empowering Credit Risk Detection in Weixin Pay with Billion-Scale Deep Graph Learning

Credit risk detection, particularly mitigating individual fraud, is crucial for maintaining the stability of digital financial ecosystems. Accurately identifying credit fraud among billions of users is critical for minimizing financial losses and safeguarding the sustainability of inclusive financial services.

diffusion safety

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