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Conference Paper: Anomaly detection in temporal graph data: An iterative tensor decomposition and masking approach

TitleAnomaly detection in temporal graph data: An iterative tensor decomposition and masking approach
Authors
Sapienza, APanisson, AWu, JTKGauvin, LCattuto, C
Issue Date2015
Citation
Proceedings of the 1st International Workshop on Advanced Analytics and Learning on Temporal Data (AALTD) co-located with The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2015), Porto, Portugal, 11 September 2015 How to Cite?
AbstractSensors and Internet-of-Things scenarios promise a wealth of interaction data that can be naturally represented by means of timevarying graphs. This brings forth new challenges for the identification and removal of temporal graph anomalies that entail complex correlations of topological features and activity patterns. Here we present an anomaly detection approach for temporal graph data based on an iterative tensor decomposition and masking procedure. We test this approach using highresolution social network data from wearable sensors and show that it successfully detects anomalies due to sensor wearing time protocols.
Persistent Identifierhttp://hdl.handle.net/10722/248213

 

DC FieldValueLanguage
dc.contributor.authorSapienza, A-
dc.contributor.authorPanisson, A-
dc.contributor.authorWu, JTK-
dc.contributor.authorGauvin, L-
dc.contributor.authorCattuto, C-
dc.date.accessioned2017-10-18T08:39:39Z-
dc.date.available2017-10-18T08:39:39Z-
dc.date.issued2015-
dc.identifier.citationProceedings of the 1st International Workshop on Advanced Analytics and Learning on Temporal Data (AALTD) co-located with The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2015), Porto, Portugal, 11 September 2015-
dc.identifier.urihttp://hdl.handle.net/10722/248213-
dc.description.abstractSensors and Internet-of-Things scenarios promise a wealth of interaction data that can be naturally represented by means of timevarying graphs. This brings forth new challenges for the identification and removal of temporal graph anomalies that entail complex correlations of topological features and activity patterns. Here we present an anomaly detection approach for temporal graph data based on an iterative tensor decomposition and masking procedure. We test this approach using highresolution social network data from wearable sensors and show that it successfully detects anomalies due to sensor wearing time protocols.-
dc.languageeng-
dc.relation.ispartofInternational Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2015-
dc.rightsCopyright © 2015 for this paper by its authors. Copying permitted for private and academic purposes-
dc.titleAnomaly detection in temporal graph data: An iterative tensor decomposition and masking approach-
dc.typeConference_Paper-
dc.identifier.emailWu, JTK: joewu@hku.hk-
dc.identifier.authorityWu, JTK=rp00517-
dc.description.naturepublished_or_final_version-
dc.identifier.scopuseid_2-s2.0-84944273871-
dc.identifier.hkuros280788-

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