Abstract: Methods and systems are for using machine learning models to locate information in an organizational graph. A search system may use techniques described herein to determine relevant data (e.g., organizational knowledge) to retrieve from a knowledge graph for input to a machine learning model. The search system may retrieve more relevant data from the knowledge graph through the use of time data that may enable the search system to avoid outdated information. The search system may also limit the data that may be used in determining an answer to a query. By doing so, the search system may be able to answer queries more efficiently (e.g., using less computing resources, less processing power, etc.).

Abstract: Methods and systems are for using machine learning models to locate information in an organizational graph. A search system may use techniques described herein to determine relevant data (e.g., organizational knowledge) to retrieve from a knowledge graph for input to a machine learning model. The search system may retrieve more relevant data from the knowledge graph through the use of time data that may enable the search system to avoid outdated information. The search system may also limit the data that may be used in determining an answer to a query. By doing so, the search system may be able to answer queries more efficiently (e.g., using less computing resources, less processing power, etc.).

Abstract: Systems, methods, and computer program products for identifying trends in behavior using embedding drift. A graph neural network may receive a network graph includes a plurality of nodes, the network graph based on a plurality of transactions for a first time interval, each transaction associated with at least one account. An embedding layer of the neural network may generate, based on the network graph, a respective embedding vector for each of the nodes. The neural network may receive a second embedding vector for each of the nodes. The neural network may determine, based on the embedding vectors and the second embedding vectors, a respective drift for each node. The neural network may determine that the drift of a first node is greater than the drift of a second node, and performing a processing operation on a first account corresponding to the first node.