The paper introduces the Graph Dynamics Model (GDM), a world model that learns stochastic latent dynamics over evolving graph topologies. GDM employs a sparse recurrent adjacency matrix for topology updates and a recurrent state‑space architecture for stochastic transitions, enabling it to handle partially observable, stochastic environments. The authors also propose the Graph Distribution Distance (GDD) metric, using maximum mean discrepancy with a graph kernel, to compare predicted and true joint graph state distributions, and demonstrate GDM’s superior performance and zero‑shot generalisation on large graphs.
By Alex Schutz, Nick Hawes, Victor-Alexandru Darvariu
The paper studies uncertainty quantification in federated Granger causality learning, where multiple parties cannot share data but need to infer predictive dependencies across clients. It addresses the challenge that clients observe different features and that cross-client edges must be estimated indirectly through iterative client‑server communication, which propagates both aleatoric and epistemic uncertainty. By deriving closed‑form variance recursions and steady‑state variances, the authors show that initial model‑parameter uncertainty diminishes asymptotically, enabling statistically principled selection of genuine cross‑client dependencies and improving edge recovery in synthetic and real industrial datasets.
By Ayush Mohanty, Nazal Mohamed, Nagi Gebraeel
arXiv:2603. 10676v2 Announce Type: replace Abstract: Industrial Control Systems (ICS) underpin critical infrastructure and face growing cyber-physical threats due to the convergence of operational technology and networked environments.
By Kosti Koistinen, Kirsi Hellsten, Joni Herttuainen, Kimmo K. Kaski
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:2609.26855v1 Announce Type: cross
Abstract: Relational Deep Learning (RDL) models multi-table databases as heterogeneous temporal graphs, and graph transformers currently achieve state-of-the-a...
By Kyaw Hpone Myint, Nan Jiang, Xiang Li, Zhe Wu, Alexandre G. R. Day, Pranab Mohanty, Giri Iyengar
arXiv:2508. 18173v2 Announce Type: replace Abstract: The discovery of symbolic governing equations is a central goal in science; yet, it remains challenging particularly for graph dynamical systems, where the network topology further shapes the system behavior.
By Riccardo Cappi, Paolo Frazzetto, Nicol\`o Navarin, Alessandro Sperduti
BrainDyn is a graph ordinary differential equation (ODE) network that models brain activity as coordinated patterns across connected regions. It uses a cellular sheaf with dynamic restriction maps to mediate region-to-region communication through learned linear transformations, rather than the standard shared feature space and scalar summation of message‑passing networks. Evaluated on resting‑state fMRI, EEG, and simulated NEST data, BrainDyn outperforms competing models in multi‑region forecasting, autoregressive generation of brain transients, and prediction of altered transients after controlled perturbations, while its learned features are expressive, interpretable, and biologically aligned.
By Siddharth Viswanath, Panayiotis Ketonis, Cassidy All, William Dai, Gabriel Escobedo, Christine Gao, Jeffrey Lee, Joao Felipe Rocha, Andrew J Steindl, Ian Adelstein, Michael Perlmutter, Dhananjay Bhaskar, Smita Krishnaswamy
arXiv:2606. 03067v1 Announce Type: cross Abstract: A recurring data mining task in complex networks is to determine how individual nodes contribute to system behavior.
By Valentina Kuskova, Dmitry Zaytsev, Michael Coppedge
arXiv:2606. 11251v1 Announce Type: new Abstract: Many multivariate dynamical systems are observed only through trajectories, leaving the mechanisms governing their joint dynamics hidden.
By Xingji Cui
arXiv:2608. 10969v1 Announce Type: new Abstract: Healthcare data, such as Intensive Care Unit (ICU) records, comprise heterogeneous multivariate time series sampled at irregular intervals with pervasive missingness.
By Ruirui Wang, Yanke Li, Manuel G\"unther, Diego Paez-Granados
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
The paper introduces a control foundation model that treats control-experiment data as dynamic graphs, allowing both response prediction and control-input generation to be handled as a unified masked prediction problem. It defines a data space of dynamic graphs with connections, signal values, and missing values, and uses the Block Diagram Transformer (BDT) to combine temporal attention with graph neural networks. Trained on mixed experimental settings, the BDT successfully stabilized an unstable system via feedback control, outperforming fine‑tuned time‑series foundation models.