arXiv Machine Learning

HGCN(O): A Self-Tuning GCN HyperModel Toolkit for Outcome Prediction in Event-Sequence Data

arXiv:2507. 22524v3 Announce Type: replace Abstract: We propose HGCN(O), a self-tuning toolkit using Graph Convolutional Network (GCN) models for event sequence prediction.

arXiv Machine Learning

Sep 15

GNN4PPM: Multi-Target Predictive Process Monitoring with Relational Graph Convolutional Networks

arXiv:2609.14534v1 Announce Type: new Abstract: Predictive Process Monitoring (PPM) aims at predicting at runtime and as early as possible the future states of a process execution. Common tasks inclu...

By Ana Costa, Johannes M\"akelburg, Luise Pufahl

rag benchmarks

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 Machine Learning

Jul 3

EHHN: An Event-driven Heterogeneous Hypergraph Network for Object-Centric Next Activity Prediction

arXiv:2607. 01785v1 Announce Type: new Abstract: Next activity prediction helps service-oriented processes anticipate upcoming steps before delays, exceptions, or service-level risks occur.

By Jiaxing Wang, Kaitao Chen, Zhubin Han, Chenyu Hou, Bin Cao, Jing Fan, Ji Zhang

benchmarks

arXiv AI

Jun 18

Graph Grounded Cross Attention Transformer Neural Network for Structurally Constrained Full Event Sequence Generation in Predictive Process Monitoring

arXiv:2606. 18726v1 Announce Type: cross Abstract: Structurally constrained event sequence generation remains challenging because generated paths must preserve transition feasibility, temporal order, termination, and attribute consistency.

By Fang Wang, Ernesto Damiani

llms benchmarks safety

arXiv AI

Sep 10

Leveraging contextual events on structure-aware next activity prediction

arXiv:2609.08622v1 Announce Type: cross Abstract: Predictive process monitoring aims at forecasting various aspects of running processes. Among the different tasks, next activity prediction represent...

By Alessandro Mele, Claudia Diamantini, Domenico Potena

arXiv AI

Jul 28

GTIN: A Unified Framework for Joint Event and Time Prediction in Temporal Graphs

arXiv:2607. 23556v1 Announce Type: cross Abstract: Temporal graphs are increasingly used to model dynamic systems in diverse domains such as social networks, financial networks, and traffic networks.

By Mohammad Ostadmohammadi, Sepehr Kazemi, Hamid R. Rabiee

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 AI

Jul 10

Towards the Explainability of Temporal Graph Networks via Memory Backtracking and Topological Attribution

arXiv:2607. 07716v1 Announce Type: cross Abstract: Temporal graphs are ubiquitous in real-world applications and Temporal Graph Networks (TGNs) have achieved superior predictive accuracy.

By Yazheng Liu, Xi Zhang, Sihong Xie, Hui Xiong

benchmarks

arXiv Machine Learning

Sep 23

Multi-Term Fourier Graph Neural Network with Sample Relationship Learning for Enhanced Remaining Useful Life Prediction

arXiv:2609.25179v1 Announce Type: new Abstract: Predicting the remaining useful life (RUL) is essential for effective predictive maintenance. Spatio-Temporal Graph Neural Networks (ST-GNNs), which ca...

By Ya Song, Laurens Bliek, Yaoxin Wu, Yingqian Zhang

benchmarks

arXiv AI

Aug 18

A Network-driven Framework for Public Event Forecasting via Dynamic Interaction Network Evolution

arXiv:2608. 15488v1 Announce Type: new Abstract: Effective public event forecasting is essential for intelligent service systems, enabling proactive risk management, adaptive resource allocation, and timely decision-making.

By Jie Wei, Yue Liu, Xiaochuan Tang, Biao Cai, Xiangtao Li, Yanmei Hu

benchmarks safety

arXiv Machine Learning

Sep 22

GraphToolbox: A Configurable Python Framework for Graph Neural Network Forecasting

arXiv:2609.24609v1 Announce Type: new Abstract: Electricity forecasting often involves spatially related signals observed over regions, substations, and feeders, and Graph Neural Networks (GNNs) prov...

By Eloi Campagne (CB), Yvenn Amara-Ouali (LMO, CELESTE), Yannig Goude (EDF R\&D), Argyris Kalogeratos (CB, ENS Paris Saclay)

safety

arXiv Machine Learning

Sep 22

SiST-GNN: Simultaneous Spatial-Temporal Message Passing for Dynamic Graph Representation Learning

SiST‑GNN introduces a simultaneous spatial‑temporal message‑passing framework for dynamic graph neural networks, fusing per‑node temporal embeddings with spatial aggregation in a single operation. By maintaining a recurrent hidden state per node and treating it as a cross‑time edge, the model jointly reasons over topology and evolution. Experiments on link‑prediction and node‑classification benchmarks show significant improvements over prior methods, achieving up to 158% gains in live‑update link prediction and outperforming discrete‑time baselines by 7–23% in dynamic node classification.

By Shubhajit Roy, Anirban Dasgupta

rag benchmarks

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