Contextual anomalies in graphs, detection and explanation
Rémi Vaudaine (Laboratoire Hubert Curien, Saint-Etienne)
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Le 24/06/2021, de 13:00 à 14:00 |
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Rémi Vaudaine |
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Title : Contextual anomalies in graphs, detection and explanation
Asbtract :
Graph anomaly detection have proved very useful in a wide range of domains. For instance, for detecting anomalous accounts (e.g. bots, terrorists, opinion spammers or social malwares) on online platforms, intrusions and failures on communication networks or suspicious and fraudulent behaviors on social networks.
However, most existing methods often rely on pre-selected features built from the graph, do not necessarily use local information and do not consider context based anomalies. To overcome these limits, we present a Context-Based Graph Anomaly Detector which makes use of local information to detect anomalous nodes of a graph in a semi-supervised way. We use Graph Attention Networks (GAT) with our custom attention mechanism to build local features, aggregate them and classify unlabeled nodes into normal or anomaly.
However, most existing methods often rely on pre-selected features built from the graph, do not necessarily use local information and do not consider context based anomalies. To overcome these limits, we present a Context-Based Graph Anomaly Detector which makes use of local information to detect anomalous nodes of a graph in a semi-supervised way. We use Graph Attention Networks (GAT) with our custom attention mechanism to build local features, aggregate them and classify unlabeled nodes into normal or anomaly.
Nevertheless, most of the models based on machine learning, particularly deep neural networks, are seen as black boxes where the output cannot be humanly related to the input in a simple way. This implies a lack of understanding of the underlying model and its results. We present a new method to explain, in a human-understandable fashion, the decision of a black-box model for anomaly detection on attributed graph data. We show that our method can recover the information that leads the model to label a node as anomalous.
Séminaire en ligne :https://ent-services.ens-lyon.fr/entVisio/quickjoin.php?hash=b41fc6e98915a6633e08151f170c83b2d9dd4b6e412e2063ee97ad19df3d04ef&meetingID=7117
ou sur webconférence de ENS (https://ent-services.ens-lyon.fr/entVisio/create-meeting.php)
Salle de conférence : MLSP_REMI_VAUDAINE
mdp: mlspseminar2021
More infiormation : https://dblp.org/pid/230/8020.html
Séminaire en ligne : (announced soon)