arXiv:2607. 03188v1 Announce Type: cross Abstract: Episode mining aims to extract subsequences of events that possess certain distinctive properties and constitute facts valuable to the user.
By Maxim Ivanov, Matvei Smirnov, Alisa Strazdina, George Chernishev
The paper introduces an unsupervised hypergraph neural network designed to detect anomalous hyperedges—higher-order associations that deviate from typical patterns. Unlike conventional graph methods that capture only pairwise relationships, this approach leverages hypergraphs to model associations among any number of entities. Experiments on real-life datasets show the model effectively identifies unusual hyperedges without requiring labeled data.
By Md. Tanvir Alam, Md. Mahmudur Rahman, Md. Fahim Arefin, Chowdhury Farhan Ahmed, Zisan Mahmud, Md. Sadman Sakib, Carson K. Leung
Anomaly detection is often applied to data stored in relational databases, yet most existing methods require flattening multiple tables into a single feature matrix. This flattening can obscure entity...
arXiv:2606. 00304v1 Announce Type: new Abstract: Graph anomaly detection methods aim to distinguish anomalous nodes.
By Yilin Liu, Hongchao Zhang, Taylor T. Johnson, Ahmad F. Taha, Meiyi Ma
arXiv:2606. 09666v1 Announce Type: new Abstract: Output space pattern sampling is a powerful alternative to exhaustive pattern mining for exploring large pattern spaces, as it enables users to focus on representative patterns drawn according to a chosen interestingness measure.
By Djawad Bekkoucha, Abdelkader Ouali, Bruno Cr\'emilleux
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