Machine learning model architecture for combining network data and sequential data

A method comprising:

• building a graph data structure storing network data from a relational data structure, wherein the relational data structure stores sequential data describing a plurality of object identifiers and relationships between the plurality of object identifiers;
• generating, from the sequential data of the relational data structure, a features matrix for the plurality of object identifiers;
• executing a plurality of machine learning model layers comprising a plurality of graph convolutional neural network (GCN) layers, wherein executing the plurality of machine learning model layers further comprises:
• executing an initial hidden layer, of the plurality of GCN layers, that takes, as input, a combination of the features matrix and the graph data structure and generates, as output, an initial hidden layer output,
• executing a first hidden layer, of a number of subsequent hidden layers subsequent to the initial hidden layer of the plurality of GCN layers, that takes, as input, the initial hidden layer output, the graph data structure, and a weights matrix and generates, as output, a first hidden layer output,
• executing the number of subsequent hidden layers that takes, as input, a preceding hidden layer output starting with the first hidden layer output, the graph data structure, and the weights matrix and generates, as output a penultimate layer output,
• executing an ultimate layer comprising a fully connected layer programmed that takes, as input, the penultimate layer output and generates, as output, an output vector comprising a plurality of sub-vectors for each of the plurality of object identifiers, wherein the plurality of sub-vectors embed effects that relationships among the plurality of object identifiers have on the features matrix;
• executing a machine learning model layer comprising a long short-term memory neural network (LSTM) that takes, as input, the output vector and generates, as output, a final output vector representing a predicted future state of the feature matrix.