IJCAI 2024poster0 citations
Implicit Anomaly Subgraph Detection (IASD) in Multi-Domain Attribute Networks
Abstract
Anomaly subgraph detection is a vital task in various real applications. However, with the advancement of AI technology, it faces new challenges: 1) Anomaly features are often deeply hidden within large datasets, and 2) Anomaly detection approaches are required to unveil the mechanisms behind anomaly generation. Our study focuses on detecting hidden anomaly subgraphs within big data and offering improved explanations for the root cause of anomalies by integrating multi-domain datasets.
DC: Data MiningDC: Knowledge Representation and ReasoningDC: Machine LearningDC: Search
BibTeX
@inproceedings{ijcai2024p970,
title = {Implicit Anomaly Subgraph Detection (IASD) in Multi-Domain Attribute Networks},
author = {Sun, Ying},
booktitle = {Proceedings of the Thirty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-24}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Kate Larson},
pages = {8510--8511},
year = {2024},
month = {8},
note = {Doctoral Consortium},
doi = {10.24963/ijcai.2024/970},
url = {https://doi.org/10.24963/ijcai.2024/970},
}