/PRNewswire/ Weights & Biases today announced two significant new enhancements - W&B Models and W&B Launch - to its industry-leading MLOps platform during.
Graph anomaly detection in this paper aims to distinguish abnormal nodes that behave differently from the benign ones accounting for the majority of graph-structured instances. Receiving increasing attention from both academia and industry, yet existing research on this task still suffers from two critical issues when learning informative anomalous behavior from graph data. For one thing, anomalies are usually hard to capture because of their subtle abnormal behavior and the shortage of background knowledge about them, which causes severe anomalous sample scarcity. Meanwhile, the overwhelming majority of objects in real-world graphs are normal, bringing the class imbalance problem as well. To bridge the gaps, this paper devises a novel Data Augmentation-based Graph Anomaly Detection (DAGAD) framework for attributed graphs, equipped with three specially designed modules: 1) an information fusion module employing graph neural network encoders to learn representations, 2) a graph data aug
Hemant Tyagi and Christophe Biernacki (MODAL project-team) are organising a two-day workshop on statistical learning for LARge scale GRaphs (LARGR), on March 9 and 10, 2023, in the amphitheatre of building B of the Inria centre of the University of Lille.
Hemant Tyagi and Christophe Biernacki (MODAL project-team) are organising a two-day workshop on statistical learning for LARge scale GRaphs (LARGR), on March 9 and 10, 2023, in the amphitheatre of building B of the Inria centre of the University of Lille.