Abstract: Probabilistic spatiotemporal forecasting comprising acquiring a time series of observed states from a real-world system, each observed state corresponding to a respective time-step in the time series and including a set of data observations of the real-world system for the respective time-step. For each of a plurality of the time steps in the time series of observed states, a hidden state is generated for the time-step based on an observed state for a prior time-step and an approximated posterior distribution generated for a hidden state for the prior time-step. The use of an approximated posterior distribution can enable improved forecasting in complex, high dimensional settings.

Abstract: System and method for processing a graph that defines a set of nodes and a set of edges, the nodes each having an associated set of node attributes, the edges each representing a relationship that connects two respective nodes, comprising: generating a first node embedding for each node by: generating, for the node and each of a plurality of neighbour nodes, a respective first edge attribute defining a respective relationship type between the node and the neighbour node based on the node attributes of the node and the node attributes of the neighbour node; generating a first neighborhood vector that aggregates information from the generated first edge attributes and the node attributes of the neighbour nodes; generating the first node embedding based on the node attributes of the node and the generated first neighborhood vector.

Abstract: A recommendation system (RS) for processing an input dataset that identifies a set of users, a set of items, and user-item interaction data about historic interactions between users in the set of users and items in the set of items. The RS is configured to: generate, based on a user-item interaction dataset, a user-user similarity dataset and an item-item similarity dataset, filter the user-user similarity dataset based on a user similarity threshold vector that includes a respective user similarity threshold value for each user, filter the item-item similarity dataset based on an item similarity threshold vector including a respective item similarity threshold value for each item generate a set of user neighbour embeddings based on the filtered user-user similarity dataset, and generating a set of item neighbour embeddings based on the filtered item-item similarity dataset.

Abstract: Method and system for processing an attributed graph that comprises a training dataset of labelled nodes and an unlabeled dataset of unlabeled nodes. The method and system includes selecting, using logistic regression, which candidate node from a plurality of possible candidate nodes included in the unlabeled dataset will minimize a risk if that candidate node is added to the training dataset; obtaining a label for the selected candidate node from a classification resource; and adding the selected candidate node and the obtained label to the training dataset as a labelled node to provide an enhanced training dataset.

Abstract: System and method for processing an observed bipartite graph that has a plurality of user nodes, a plurality of item nodes, and an observed graph topology that defines edges connecting at least some of the user nodes to some of the item nodes such that at least some nodes have node neighbourhoods comprising edge connections to one or more other nodes. A plurality of random graph topologies are derived that are realizations of the observed graph topology by replacing the node neighbourhoods of at least some nodes with the node neighbourhoods of other nodes. A non-linear function is trained using the plurality of user nodes, plurality of item nodes and plurality of random graph topologies to learn user node embeddings and item node embeddings for the plurality of user nodes and plurality of item nodes, respectively.

Abstract: Method and system for predicting labels for nodes in an observed graph, including deriving a plurality of random graph realizations of the observed graph; learning a predictive function using the random graph realizations; predicting label probabilities for nodes of the random graph realizations using the learned predictive function; and averaging the predicted label probabilities to predict labels for the nodes of the observed graph.

Abstract: Method and system for detecting potentially perturbed nodes in a graph that comprises potentially perturbed nodes and clean nodes, comprising: calculating, for each of a plurality of nodes of the graph, a discrepancy value in respect of the node, wherein the discrepancy value for each node indicates a statistical discrepancy for classification probabilities associated with the node and classification probabilities associated with neighbouring nodes; fitting a statistical distribution for the discrepancy values for the clean nodes; determining a detection threshold for potentially perturbed nodes based on the statistical distribution; and identifying nodes having a discrepancy value greater than the detection threshold as potentially perturbed nodes.