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In mathematics, low-rank approximation is a minimization problem, in which the cost function measures the fit between a given matrix (the data) and an approximating matrix (the optimization variable), subject to a constraint that the approximating matrix has reduced rank. The problem is used for mathematical modeling and data compression. The rank constraint is related to a constraint on the complexity of a model that fits the data. In applications, often there are other constraints on the approximating matrix apart from the rank constraint, e.g., non-negativity and Hankel structure.
Graph anomaly detection method based on low-rank contrastive learning and reconstruction
PendingCN122156934ACharacter and pattern recognitionNeural learning methodsSingular value decompositionAlgorithm
The application discloses a kind of based on low rank contrast learning and reconstruction graph anomaly detection method, comprising: 1) obtain graph data, and generate low rank graph data by SVD singular value decomposition dimension reduction, using restart random walk algorithm to carry out subgraph sampling, obtain original view and low rank view;2) on original view and low rank view, construct contrast pair and carry out contrast learning, obtain contrast learning loss;3) low rank attribute reconstruction is carried out on original view and low rank view, and the final reconstruction loss of original view and low rank view is obtained;4) the graph neural network model is trained in combination with contrast learning loss and reconstruction loss;5) according to the trained graph neural network model, the abnormality of node in the graph to be measured is judged, and the potential abnormal node is determined.The application generates low rank view by low rank approximation to node attribute and topological structure, effectively filters the interference of abnormal node and noise on the basis of retaining the original structure of graph.
Owner:SOUTH CHINA UNIV OF TECH