Abstract: One or more implementations of the present specification provide risk control of transactions based on a graphical structure model. A graphical structure model trained by using labeled samples is obtained. The graphical structure model is defined based on a transaction data network that includes nodes representing entities in a transaction and edges representing relationships between the entities. Each labeled sample includes a label indicating whether a node corresponding to the labeled sample is a risky transaction node. The graphical structure model is configured to iteratively calculate an embedding vector of the node in a latent feature space based on an original feature of the node or a feature of an edge associated with the node. An embedding vector of an input sample is calculated by using the graphical structure model. Transaction risk control is performed on the input sample based on the embedding vector.

Abstract: A graphical structure model trained by using labeled samples is obtained. The graphical structure model is defined based on an enterprise relationship network that includes nodes and edges. Each labeled sample includes a label indicating whether a corresponding node is a risky credit node. The graphical structure model is configured to iteratively calculate an embedding vector of at least one node in a hidden feature space based on an original feature of the at least one node and/or a feature of an edge associated with the at least one node. An embedding vector corresponding to a test-sample is calculated by using the graphical structure model. Credit risk analysis is performed on the test-sample. The credit risk analysis is performed based on a feature of the test-sample represented in the embedding vector. A node corresponding to the test-sample is labeled as a credit risk node.

Abstract: A graphical structure model trained with labeled samples is obtained. The graphical structure model is defined based on an account relationship network that comprises a plurality of nodes and edges. The edges correspond to relationships between adjacent nodes. Each labeled sample comprises a label indicating whether a corresponding node is an abnormal node. The graphical structure model is configured to iteratively calculate, for at least one node of the plurality of nodes, an embedding vector in a hidden feature space based on an original feature of the least one node and/or a feature of an edge associated with the at least one node. A first embedding vector that corresponds to a to-be-tested sample is calculated using the graphical structure model. Abnormal account prevention and control is performed on the to-be-tested sample based on the first embedding vector.

Abstract: A graphical structure model trained with labeled samples is obtained. The graphical structure model is defined based on an account relationship network that comprises a plurality of nodes and edges. The edges correspond to relationships between adjacent nodes. Each labeled sample comprises a label indicating whether a corresponding node is an abnormal node. The graphical structure model is configured to iteratively calculate, for at least one node of the plurality of nodes, an embedding vector in a hidden feature space based on an original feature of the least one node and/or a feature of an edge associated with the at least one node. A first embedding vector that corresponds to a to-be-tested sample is calculated using the graphical structure model. Abnormal account prevention and control is performed on the to-be-tested sample based on the first embedding vector.

Abstract: A graphical structure model trained with labeled samples is obtained. The graphical structure model is defined based on an account relationship network that comprises a plurality of nodes and edges. The edges correspond to relationships between adjacent nodes. Each labeled sample comprises a label indicating whether a corresponding node is an abnormal node. The graphical structure model is configured to iteratively calculate, for at least one node of the plurality of nodes, an embedding vector in a hidden feature space based on an original feature of the least one node and/or a feature of an edge associated with the at least one node. A first embedding vector that corresponds to a to-be-tested sample is calculated using the graphical structure model. Abnormal account prevention and control is performed on the to-be-tested sample based on the first embedding vector.

Abstract: A graphical structure model trained by using labeled samples is obtained. The graphical structure model is defined based on an enterprise relationship network that includes nodes and edges. Each labeled sample includes a label indicating whether a corresponding node is a risky credit node. The graphical structure model is configured to iteratively calculate an embedding vector of at least one node in a hidden feature space based on an original feature of the at least one node and/or a feature of an edge associated with the at least one node. An embedding vector corresponding to a test-sample is calculated by using the graphical structure model. Credit risk analysis is performed on the test-sample. The credit risk analysis is performed based on a feature of the test-sample represented in the embedding vector. A node corresponding to the test-sample is labeled as a credit risk node.

Abstract: One or more implementations of the present specification provide risk control of transactions based on a graphical structure model. A graphical structure model trained by using labeled samples is obtained. The graphical structure model is defined based on a transaction data network that includes nodes representing entities in a transaction and edges representing relationships between the entities. Each labeled sample includes a label indicating whether a node corresponding to the labeled sample is a risky transaction node. The graphical structure model is configured to iteratively calculate an embedding vector of the node in a latent feature space based on an original feature of the node or a feature of an edge associated with the node. An embedding vector of an input sample is calculated by using the graphical structure model. Transaction risk control is performed on the input sample based on the embedding vector.

Abstract: A graphical structure model trained with labeled samples is obtained. The graphical structure model is defined based on an account relationship network that comprises a plurality of nodes and edges. The edges correspond to relationships between adjacent nodes. Each labeled sample comprises a label indicating whether a corresponding node is an abnormal node. The graphical structure model is configured to iteratively calculate, for at least one node of the plurality of nodes, an embedding vector in a hidden feature space based on an original feature of the least one node and/or a feature of an edge associated with the at least one node. A first embedding vector that corresponds to a to-be-tested sample is calculated using the graphical structure model. Abnormal account prevention and control is performed on the to-be-tested sample based on the first embedding vector.