Abstract: A temporal graph model is provided that can handle all six graph updates cases: vertex created; vertex deleted; vertex property updated; edge created; edge deleted; and edge property updated. A memory efficient temporal graph storage solution stores the delta change of graph evolution at each update (e.g., instead of storing the whole graph snapshot at each time). Temporal graph querying techniques are described that can determine what the graph looks like at a given time in the past and can perform any graph query on that graph's snapshot at that time as if time traveling back to that time. Also provided are event sourcing techniques of recording and tracing temporal graph updates that make the evolution of the temporal graph explainable. Further provided is a seamless integration with a massive parallel graph processing and storage engine that fully leverages the multicore and multinode cluster computation and storage resources.