Methods and systems for temporal graph representation learning based on node-level temporal point processes

A server system, comprising:

• a communication interface;
• a memory comprising executable instructions;
• and
• a processor communicably coupled to the communication interface and the memory, the processor configured to cause the server system to at least:
• access historical interaction data comprising a plurality of interactions among a plurality of entities from a database;
• generate a temporal graph based, at least in part, on the historical interaction data, the temporal graph representing a computer-based graph representation of the plurality of entities as nodes and time-ordered event sequences between nodes as temporal edges;
• predict likelihoods of future interaction occurrences among the plurality of entities based, at least in part, on a pre-trained temporal point process (TPP) based graph neural network (GNN) model, the likelihoods of the future interaction occurrences determined by executing a plurality of operations for each node in a graph traversal manner, the plurality of operations comprising:
• determine a plurality of edge embeddings associated with a node based, at least in part, on node features of the node and a plurality of direct neighbor nodes of the node;
• generate edge-contextualized node embeddings of the node corresponding to the plurality of edge embeddings based, at least in part, on a neural network model;
• and
• compute a likelihood of future interaction occurrences associated with the node based, at least in part, on the edge-contextualized node embeddings and a conditional intensity function;
• and
• execute at least one of a plurality of graph context prediction tasks based, at least in part, on the likelihoods of future interaction occurrences among the plurality of entities.

A non-transitory computer-readable storage medium comprising computer-executable instructions that, when executed by at least a processor of a server system, cause the server system to perform a method comprising:

• accessing historical interaction data comprising a plurality of interactions among a plurality of entities from a database;
• generating a temporal graph based, at least in part, on the historical interaction data, the temporal graph representing a computer-based graph representation of the plurality of entities as nodes and time-ordered event sequences between nodes as temporal edges;
• predicting likelihoods of future interaction occurrences among the plurality of entities based, at least in part, on a pre-trained temporal point process (TPP) based graph neural network (GNN) model, the likelihoods of the future interaction occurrences determined by executing a plurality of operations for each node in a graph traversal manner, the plurality of operations comprising:
• determining a plurality of edge embeddings associated with a node based, at least in part, on node features of the node and a plurality of direct neighbor nodes of the node;
• generating edge-contextualized node embeddings of the node corresponding to the plurality of edge embeddings based, at least in part, on a neural network model;
• and
• computing a likelihood of future interaction occurrences associated with the node based, at least in part, on the edge-contextualized node embeddings and a conditional intensity function;
• and
• executing at least one of a plurality of graph context prediction tasks based, at least in part, on the likelihoods of future interaction occurrences among the plurality of entities.