Scalable Self-Supervised Graph Clustering

A method of applying a machine learning model, the method comprising:

• determining an encoded graph output by applying a trained machine learning model to a graph structure input and one or more feature attribute inputs, wherein the trained machine learning model comprises a graph convolutional network layer, wherein the machine learning model outputs an encoded graph based on one or more learnable parameter values of the graph convolutional network layer, wherein the one or more learnable parameter values of a graph convolutional network layer of the trained machine learning model were determined by applying a contrastive loss function to a plurality of positive samples selected through random walks along one or more paths of an encoded graph and a plurality of negative samples selected from the encoded graph by randomly sampling one or more nodes of the encoded graph;
• and
• applying a clustering algorithm to the encoded graph output to determine one or more graph clusters, wherein each graph cluster comprises one or more nearby nodes of the graph structure input with similar feature attributes.