System, method, and computer program product for dynamic node classification in temporal-based machine learning classification models

A system comprising at least one processor, the at least one processor being programmed or configured to:

• receive graph data of a discrete time dynamic graph comprising a plurality of graph snapshots, wherein each graph snapshot of the plurality of graph snapshots is associated with an instance of the discrete time dynamic graph at a time step in a first time period, and wherein each graph snapshot of the plurality of graph snapshots comprises a plurality of nodes, a plurality of edges, and a node attribute matrix;
• receive a plurality of node classifications associated with all nodes in the discrete time dynamic graph, wherein each node classification of the plurality of node classifications is associated with a node of the plurality of nodes in each graph snapshot of the plurality of graph snapshots;
• convert the discrete time dynamic graph to a time-augmented spatio-temporal graph based on the plurality of edges of each graph snapshot of the plurality of graph snapshots;
• generate an adjacency matrix of at least one adjacency matrix based on a temporal walk of the time-augmented spatio-temporal graph, the adjacency matrix comprising a plurality of adjacency matrices positioned along a diagonal of the adjacency matrix, wherein each adjacency matrix of the plurality of adjacency matrices is associated with a graph snapshot of the plurality of graph snapshots at a separate time step in the first time period, and wherein the adjacency matrix of the at least one adjacency matrix comprises at least one copied matrix of one or more adjacency matrices of the plurality of adjacency matrices;
• generate at least one adaptive information transition matrix based on the at least one adjacency matrix;
• determine a plurality of feature vectors based on the plurality of nodes and the node attribute matrix of each graph snapshot of the plurality of graph snapshots, wherein each feature vector of the plurality of feature vectors is associated with a node of the discrete time dynamic graph;
• generate a plurality of initial node representations, wherein each initial node representation of the plurality of initial node representations is based on a feature vector of the plurality of feature vectors and a node associated with the feature vector;
• propagate the plurality of initial node representations across a plurality of information propagation layers using the at least one adaptive information transition matrix, to produce a plurality of final node representations based on an output of a final layer of the plurality of information propagation layers;
• and
• classify at least one node of the discrete time dynamic graph in a time step of a second time period subsequent to the first time period based on the plurality of final node representations.

A computer program product comprising at least one non-transitory computer-readable medium comprising one or more instructions that, when executed by at least one processor, cause the at least one processor to:

• receive graph data of a discrete time dynamic graph comprising a plurality of graph snapshots, wherein each graph snapshot of the plurality of graph snapshots is associated with an instance of the discrete time dynamic graph at a time step in a first time period, and wherein each graph snapshot of the plurality of graph snapshots comprises a plurality of nodes, a plurality of edges, and a node attribute matrix;
• receive a plurality of node classifications associated with all nodes in the discrete time dynamic graph, wherein each node classification of the plurality of node classifications is associated with a node of the plurality of nodes in each graph snapshot of the plurality of graph snapshots;
• convert the discrete time dynamic graph to a time-augmented spatio-temporal graph based on the plurality of edges of each graph snapshot of the plurality of graph snapshots;
• generate an adjacency matrix of at least one adjacency matrix based on a temporal walk of the time-augmented spatio-temporal graph, the adjacency matrix comprising a plurality of adjacency matrices positioned along a diagonal of the adjacency matrix, wherein each adjacency matrix of the plurality of adjacency matrices is associated with a graph snapshot of the plurality of graph snapshots at a separate time step in the first time period, and wherein the adjacency matrix of the at least one adjacency matrix comprises at least one copied matrix of one or more adjacency matrices of the plurality of adjacency matrices;
• generate at least one adaptive information transition matrix based on the at least one adjacency matrix;
• determine a plurality of feature vectors based on the plurality of nodes and the node attribute matrix of each graph snapshot of the plurality of graph snapshots, wherein each feature vector of the plurality of feature vectors is associated with a node of the discrete time dynamic graph;
• generate a plurality of initial node representations, wherein each initial node representation of the plurality of initial node representations is based on a feature vector of the plurality of feature vectors and a node associated with the feature vector;
• propagate the plurality of initial node representations across a plurality of information propagation layers using the at least one adaptive information transition matrix, to produce a plurality of final node representations based on an output of a final layer of the plurality of information propagation layers;
• and
• classify at least one node of the discrete time dynamic graph in a time step of a second time period subsequent to the first time period based on the plurality of final node representations.