Graph-based labeling of heterogenous digital content items

A system comprising:

• a processor;
• and
• memory coupled to the processor, wherein the memory comprises instructions that when executed by the processor cause the processor to:
• for a set of digital content items received from user systems by an application system, generate and store a content graph, wherein:
• the application system is capable of distributing digital content to the user systems over a network based on a social graph and a set of spam labels;
• and
• the content graph comprises labeled nodes that correspond to a subset of the set of digital content items that have spam labels, unlabeled nodes that correspond to a subset of the set of digital content items that do not have spam labels, and edges between nodes of the content graph;
• query the content graph to retrieve edge data for an edge between an unlabeled node and an adjacent node, wherein:
• the unlabeled node corresponds to a digital content item that does not have a spam label;
• the adjacent node corresponds to a different digital content item;
• and
• the edge data indicates a relationship between the unlabeled node and the adjacent node;
• and
• responsive to a set of inputs comprising the retrieved edge data and embedding data associated with the unlabeled node, by a machine learning, assign a label of the set of spam labels to the unlabeled node, wherein:
• the embedding data comprises output of a cross-modality embedding model;
• the output of the cross-modality embedding model is connected to input of the machine learning model;
• the machine learning model comprises a graph transformer-based neural network having connected modality-specific layers;
• the graph transformer-based neural network is trained to determine inter-content item relationships based on the labeled nodes and the edges of the content graph;
• and
• the label is assigned to the unlabeled node by the connected modality-specific layers of the trained graph transformer-based neural network performing graph-based propagation of label predictions from labeled nodes to unlabeled nodes of different modalities based on edge relationships between the nodes in the content graph.

A non-transitory machine-readable medium comprising instructions that when executed by a processor cause the processor to:

• for a set of digital content items received from user systems by an application system, generate and store a content graph, wherein:
• the application system is capable of distributing digital content to the user systems over a network based on a social graph and a set of spam labels;
• and
• the content graph comprises labeled nodes that correspond to a subset of the set of digital content items that have spam labels, unlabeled nodes that correspond to a subset of the set of digital content items that do not have spam labels, and edges between nodes of the content graph;
• query the content graph to retrieve edge data for an edge between an unlabeled node and an adjacent node, wherein:
• the unlabeled node corresponds to a digital content item that does not have a spam label;
• the adjacent node corresponds to a different digital content item;
• and
• the edge data indicates a relationship between the unlabeled node and the adjacent node;
• and
• responsive to a set of inputs comprising the retrieved edge data and embedding data associated with the unlabeled node, by a machine learning, assign a label of the set of spam labels to the unlabeled node, wherein:
• the embedding data comprises output of a cross-modality embedding model;
• the output of the cross-modality embedding model is connected to input of the machine learning model;
• the machine learning model comprises a graph transformer-based neural network having connected modality-specific layers;
• the graph transformer-based neural network is trained to determine inter-content item relationships based on the labeled nodes and the edges of the content graph;
• and
• the label is assigned to the unlabeled node by the connected modality-specific layers of the trained graph transformer-based neural network performing graph-based propagation of label predictions from labeled nodes to unlabeled nodes of different modalities based on edge relationships between the nodes in the content graph.