Graph neural networks for graphs with heterophily: A survey
Recent years have witnessed fast developments of graph neural networks (GNNs) that have
benefited myriads of graph analytic tasks and applications. In general, most GNNs depend …
benefited myriads of graph analytic tasks and applications. In general, most GNNs depend …
Generalized video anomaly event detection: Systematic taxonomy and comparison of deep models
Video Anomaly Detection (VAD) serves as a pivotal technology in the intelligent surveillance
systems, enabling the temporal or spatial identification of anomalous events within videos …
systems, enabling the temporal or spatial identification of anomalous events within videos …
Adbench: Anomaly detection benchmark
Given a long list of anomaly detection algorithms developed in the last few decades, how do
they perform with regard to (i) varying levels of supervision,(ii) different types of anomalies …
they perform with regard to (i) varying levels of supervision,(ii) different types of anomalies …
Rethinking graph neural networks for anomaly detection
Abstract Graph Neural Networks (GNNs) are widely applied for graph anomaly detection. As
one of the key components for GNN design is to select a tailored spectral filter, we take the …
one of the key components for GNN design is to select a tailored spectral filter, we take the …
MGLNN: Semi-supervised learning via multiple graph cooperative learning neural networks
In many machine learning applications, data are coming with multiple graphs, which is
known as the multiple graph learning problem. The problem of multiple graph learning is to …
known as the multiple graph learning problem. The problem of multiple graph learning is to …
An efficient federated distillation learning system for multitask time series classification
This article proposes an efficient federated distillation learning system (EFDLS) for multitask
time series classification (TSC). EFDLS consists of a central server and multiple mobile …
time series classification (TSC). EFDLS consists of a central server and multiple mobile …
Evaluating post-hoc explanations for graph neural networks via robustness analysis
This work studies the evaluation of explaining graph neural networks (GNNs), which is
crucial to the credibility of post-hoc explainability in practical usage. Conventional evaluation …
crucial to the credibility of post-hoc explainability in practical usage. Conventional evaluation …
Bond: Benchmarking unsupervised outlier node detection on static attributed graphs
Detecting which nodes in graphs are outliers is a relatively new machine learning task with
numerous applications. Despite the proliferation of algorithms developed in recent years for …
numerous applications. Despite the proliferation of algorithms developed in recent years for …
A comprehensive survey on deep clustering: Taxonomy, challenges, and future directions
Clustering is a fundamental machine learning task which has been widely studied in the
literature. Classic clustering methods follow the assumption that data are represented as …
literature. Classic clustering methods follow the assumption that data are represented as …
Interpretable anomaly detection with diffi: Depth-based feature importance of isolation forest
Anomaly Detection is an unsupervised learning task aimed at detecting anomalous
behaviors with respect to historical data. In particular, multivariate Anomaly Detection has an …
behaviors with respect to historical data. In particular, multivariate Anomaly Detection has an …