arXiv Machine Learning

Spatiotemporal Graph Transformer for 3D Neighborhood Interaction and Quality Prediction in Metal Additive Manufacturing

arXiv:2606. 10227v1 Announce Type: new Abstract: Metal additive manufacturing enables the fabrication of complex parts, but achieving consistent build quality remains challenging due to interactions induced by repeated layer-wise melting, solidification, and reheating across the 3D build.

arXiv Machine Learning

Sep 29

MeshGraphNet-Transformer: Scalable Mesh-based Learned Simulation for Solid Mechanics

MeshGraphNet-Transformer (MGN‑T) is a new architecture that fuses Transformers’ global modeling with MeshGraphNets’ geometric inductive bias, keeping a mesh‑based graph representation. It replaces iterative message passing with a physics‑attention Transformer that updates all nodal states simultaneously, enabling efficient learning on high‑resolution meshes with diverse geometries, topologies, and boundary conditions. MGN‑T accurately models impact dynamics, self‑contact, plasticity, and multivariate outputs, outperforming state‑of‑the‑art methods on classical benchmarks while using far fewer parameters.

By Mikel M. Iparraguirre, Iciar Alfaro, David Gonzalez, Elias Cueto

llms benchmarks safety

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 1

Deep graph kernel point processes over networks

arXiv:2306.11313v5 Announce Type: replace-cross Abstract: Point process models are widely used for continuous-time discrete-event data, where each data point includes time and additional information...

By Zheng Dong, Matthew Repasky, Xiuyuan Cheng, Yao Xie

benchmarks

arXiv AI

Jul 28

A Survey of Graph Transformers: Architectures, Theories and Applications

arXiv:2502. 16533v3 Announce Type: replace-cross Abstract: Graph Transformers (GTs) have demonstrated a strong capability in modeling graph structures by addressing the intrinsic limitations of graph neural networks (GNNs), such as over-smoothing and over-squashing.

By Chaohao Yuan, Kangfei Zhao, Ercan Engin Kuruoglu, Liang Wang, Tingyang Xu, Wenbing Huang, Deli Zhao, Hong Cheng, Yu Rong

llms nlp

arXiv AI

Jul 28

Integrating Factual and Normative Industrial Knowledge via Constraint-Aware Graph Attention for Process Plan Recommendation

arXiv:2607. 24213v1 Announce Type: new Abstract: Integrating heterogeneous industrial knowledge, including factual relations and decision constraints, remains a core challenge in industrial information systems.

By Yuntong Chen, Yingqi Li, Yingying Xiao, Ziang Wang, Zewei Liu, Jiahao Liu, Xitian Tian, Lijiang Huang

efficiency benchmarks

arXiv Machine Learning

Oct 5

Learning the Latent Structure: A Feature-Centric Approach to Graph Data Augmentation

arXiv:2610.02517v1 Announce Type: new Abstract: Graph-structured data plays a pivotal role in modeling complex relationships. However, real-world graphs are often incomplete due to data collection an...

By Yu Song, Zhigang Hua, Yan Xie, Bingheng Li, Jingzhe Liu, Bo Long, Jiliang Tang, Hui Liu

rag benchmarks

arXiv AI

Jul 14

Local Message-Passing for Discrete Graph Generation

arXiv:2603. 08825v2 Announce Type: replace-cross Abstract: Discrete graph generation has emerged as a powerful paradigm for modeling graph-structured data, yet state of the art models often rely on Graph Transformers or higher order architectures.

By Jay Revolinsky, Harry Shomer, Jiliang Tang

llms diffusion benchmarks

arXiv AI

Sep 7

Dynamic Heterogeneous Graph Representation Learning: A Survey

The article surveys Dynamic Heterogeneous Graph Representation Learning (DHGRL), a field that tackles the challenges of modeling evolving, multi‑type networks. It introduces a unified definition covering both discrete‑time and continuous‑time DHGs, and proposes an algorithm‑centric taxonomy that groups methods into embedding‑based, GNN‑based, and Transformer‑based approaches, highlighting their biases toward temporal granularity. The survey also reviews key applications, datasets, benchmarks, and outlines future research directions.

By Huan Liu, Pengfei Jiao, Jie Yin, Hongjiang Chen, Zhidong Zhao

llms rag benchmarks

arXiv Machine Learning

Jun 3

TIDFormer: Exploiting Temporal and Interactive Dynamics Makes A Great Dynamic Graph Transformer

arXiv:2506. 00431v2 Announce Type: replace Abstract: Due to the proficiency of self-attention mechanisms (SAMs) in capturing dependencies in sequence modeling, several existing dynamic graph neural networks (DGNNs) utilize Transformer architectures with various encoding designs to capture sequential evolutions of dynamic graphs.

By Jie Peng, Zhewei Wei, Yuhang Ye

llms rag benchmarks safety

arXiv Machine Learning

Jun 2

AdaKernel: Learning Adaptive Kernel Parameters for Spatiotemporal Graph Neural Networks

arXiv:2606. 01283v1 Announce Type: new Abstract: Modeling spatial dependencies is central to spatiotemporal data analysis using Graph Neural Networks (GNNs).

By Zhongyue Zhang, Guangyin Jin, Yuxuan Liang, Suwan Yin, Yuankai Wu

arXiv Machine Learning

Sep 1

Generation of High-Level Concepts in 3D Scene Graphs via Autoregressive Diffusion

arXiv:2608.28733v1 Announce Type: cross Abstract: Indoor 3D Scene Graphs (3DSGs) represent environments as multi-layer hierarchies that connect observed geometric primitives (e.g., planes) to higher-...

By Jose Andres Millan-Romera, Samuel Cognolato, Holger Voos, Jose Luis Sanchez-Lopez, Luciano Serafini

diffusion robotics

arXiv Machine Learning

Jun 19

Towards Modality-imbalanced Federated Graph Learning: A Data Synthesis-based Approach

arXiv:2606. 20382v1 Announce Type: new Abstract: MultiModal Federated Graph Learning (MM-FGL) offers a natural collaborative training paradigm, but its practical deployment is challenged by two granularities of modality imbalance.

By Zhengyu Wu, Hongchao Qin, Xunkai Li, Zekai Chen, Rong-Hua Li, Guoren Wang

multimodal safety

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