aiqbench analysis
This research introduces an unsupervised hypergraph neural network designed to detect anomalies within higher-order data associations, addressing a significant gap in current graph learning literature. By moving beyond pairwise relationships, the model effectively identifies unusual patterns in complex datasets without relying on labeled training data. The work advances the field of machine learning by providing a robust framework for analyzing intricate entity interactions that standard graphs cannot capture.
Hyperedge Anomaly Detection with Hypergraph Neural Network
arXiv:2412.05641v2 Announce Type: replace-cross Abstract: Hypergraph is a data structure that enables us to model higher-order associations among data entities. Conventional graph-structured data can represent pairwise relationships only, whereas hypergraph enables us to associate any number of entities, which is essential in many real-life applications. Hypergraph learning algorithms have been well-studied for numerous problem settings, such as node classification, link prediction, etc. However, much less research has been conducted on anomaly detection from hypergraphs. Anomaly detection identifies events that deviate from the usual pattern and can be applied to hypergraphs to detect unusual higher-order associations. In this work, we propose an end-to-end hypergraph neural network-based model for identifying anomalous associations in a hypergraph. Our proposed algorithm operates in an unsupervised manner without requiring any labeled data. Extensive experimentation on several real-life datasets demonstrates the effectiveness of our model in detecting anomalous hyperedges.
Abstract:Hypergraph is a data structure that enables us to model higher-order associations among data entities. Conventional graph-structured data can represent pairwise relationships only, whereas hypergraph enables us to associate any number of entities, which is essential in many real-life applications. Hypergraph learning algorithms have been well-studied for numerous problem settings, such as node classification, link prediction, etc. However, much less research has been conducted on anomaly detection from hypergraphs. Anomaly detection identifies events that deviate from the usual pattern and can be applied to hypergraphs to detect unusual higher-order associations. In this work, we propose an end-to-end hypergraph neural network-based model for identifying anomalous associations in a hypergraph. Our proposed algorithm operates in an unsupervised manner without requiring any labeled data. Extensive experimentation on several real-life datasets demonstrates the effectiveness of our model in detecting anomalous hyperedges.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Social and Information Networks (cs.SI)
arXiv:2412.05641 [cs.LG]
(or arXiv:2412.05641v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2412.05641 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 237 words
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