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ARES: Anomaly Recognition Model for Edge Streams
(ACM, 2026-04-20) Mungari, Simone; Bifet, Albert; Manco, Giuseppe; Pfahringer, Bernhard
Many real-world scenarios involving streaming information can be represented as temporal graphs, where data flows through dynamic changes in edges over time. Anomaly detection in this context has the objective of identifying unusual temporal connections within the graph structure. Detecting edge anomalies in real time is crucial for mitigating potential risks. Unlike traditional anomaly detection, this task is particularly challenging due to concept drifts, large data volumes, and the need for real-time response. To face these challenges, we introduce ARES, an unsupervised anomaly detection framework for edge streams. ARES combines Graph Neural Networks (GNNs) for feature extraction with Half-Space Trees (HST) for anomaly scoring. GNNs capture both spike and burst anomalous behaviors within streams by embedding node and edge properties in a latent space, while HST partitions this space to isolate anomalies efficiently. ARES operates in an unsupervised way without the need for prior data labeling. To further validate its detection capabilities, we additionally incorporate a simple yet effective supervised thresholding mechanism. This approach leverages statistical dispersion among anomaly scores to determine the optimal threshold using a minimal set of labeled data, ensuring adaptability across different domains. We validate ARES through extensive evaluations across several real-world cyber-attack scenarios, comparing its performance against existing methods while analyzing its space and time complexity. The code used to perform the experiments is publicly available at https://github.com/AnomalyRecognitionModelForEdgeStreams/ARES.
Adaptive approaches towards fully incremental prediction interval for data stream regression
(Springer Nature, 2026) Sun, Yibin; Pfahringer, Bernhard; Gomes, Heitor Murilo; Bifet, Albert
Prediction intervals (PIs) are a practical tool for uncertainty quantification in regression, but comparatively little work has addressed fully incremental PI generation for data streams. In streaming settings, data arrive continuously, each instance is typically processed once, and concept drift can quickly invalidate a previously well-calibrated interval. These properties make many batch PI methods and window-based adaptations difficult to apply efficiently. This paper studies Adaptive Prediction Interval (AdaPI), an online post-calibration framework that adjusts interval width according to observed coverage. We instantiate the framework with a fully incremental variant of Mean and Variance Estimation (MVE) and investigate three adaptive scaling functions. We also adopt an evaluation perspective that jointly considers coverage accuracy and interval width. Experiments on a collection of real-world and synthetic regression streams show that AdaPI can often move coverage closer to the desired confidence level while maintaining competitive interval width; under the default 95% confidence setting and coverage-heavy CING weighting, the linear variant frequently gives the strongest empirical coverage–width trade-off among the three adaptive strategies.
Co-designing smart cities: A case study in the New Zealand context
(ACM, 2025) Turner, Jessica; Jones, Ben E.; Everitt, Aluna; König, Jemma L.; Fredericks, Joel; Yoo, Soojeong; Tran, Tram Thi Minh; Pantidi, Nadia; Hoang, Thuong; Hoggenmueller, Marius; Caldwell, Glenda; Tag, Benjamin; Andres, Josh; Davis, Hilary; Boden, Marie; Zhu, Howe; Harman, Joel; Rahman, Jessica
Designing smart cities that align with community needs requires both innovation and active participation. However, engaging the community can be challenging due to the diverse range of stakeholders, their requirements and hesitation to participate. Co-design offers a collaborative solution, involving the community from the outset, and ensuring designs meet expectations. This research explores the feasibility of co-design approaches for smart city development in New Zealand, using a local council as a case study. We conducted an online survey with 248 participants to assess public attitudes. Survey results highlighted strong interest in addressing traffic congestion, environmental monitoring and public safety. Using these results we conducted a co-design workshop (n=13) to encourage ideation. The co-design process generated innovative concepts, evaluated based on cost, deliverability, and novelty. Findings demonstrate the potential of co-design methodologies to bridge the gap between community needs and smart city technology, offering a foundation for future urban development initiatives.
On class operators for the lower radical class and semisimple closure constructions
(Springer Nature, 2024) McConnell, N.R.; McDougall, R.G.; Stokes, Tim E.; Thornton, L.K.
We construct the lower radical class and the semisimple closure for a given class using class operators and detail some of the properties of these operators and their interplay with the operators already used in radical theory. The setting is the class of algebras introduced by Puczy lowski which ensures the results hold in groups, multi-operator groups such as rings, as well as loops and hoops.
Concept drift detection in delayed and partially labeled data streams: An experimental survey
(Elsevier, 2026) Alencar, Brenno; Cassales, Guilherme; Gomes, Heitor Murilo; Prazeres, Cássio; Rios, Tatiane N.; Bifet, Albert; Rios, Ricardo A.
Fully labeled data is the ideal scenario for supervised model training. However, labels might be scarce in many situations, specially when large data is produced as an open-ended stream. Moreover, in dynamic streams, it is assumed that the underlying process generating the data is non-stationary, changing its behavior over time. Therefore, concepts learned by the classification model are likely to change, and non-adaptive models will suffer performance degradation, a phenomenon known as concept drift. The challenge of recognizing and reacting to such changes is even more complex by facing streams with partial or delayed labels. This refers to realistic scenarios where the true label of an incoming point is not immediately available (delay) or might never be available (partial). In this work, we focus on investigating the performance of concept drift detectors in handling delayed and partially labeled data streams. We categorize the main methods from the literature, providing a taxonomy of concept drift detectors and an overview of the most influential approaches for evaluating these detectors under conditions of delayed or partial labeling. Finally, we conduct a series of experiments to analyze the performance of these detectors in scenarios with label scarcity. Concluding, the survey discusses its main limitations and offers insights into potential avenues for future research in the field.