Abstract: Systems and methods are provided for performing temporal graph computing. One method may include ordering out edges in a candidate edge set of a vertex in deceasing time, grouping the out edges into a plurality of trunks with at least one of the plurality of trunks being a multi-edge trunk having two or more edges, generating a plurality of alias tables to record content of the plurality of trunks, performing inverse transform sampling on the plurality of trunks to choose a first trunk of interest, determining that the first trunk of interest is a complete multi-edge trunk with a plurality of edges arranged in a plurality of buckets and each bucket has an average weight of a total weight of the plurality of edges and sampling an edge from the plurality of edges by alias table sampling.

Abstract: Systems and methods are provided for performing temporal graph computing. One method may comprise receiving an input temporal graph that have a plurality of edges with each edge connecting from one vertex instance to another vertex instance, generating in-vertices and out-vertices for each vertex instance, merging the in-vertices and out-vertices into hub vertices for each vertex instance and generating a directed acyclic graph (DAG), receiving a minimum path problem, and scanning the DAG once to provide a solution to the minimum path problem. The merging of vertices and generation of the DAG may be performed by sorting all out-vertices using a 2-dimensional radix sort, generating a respective set of hub vertices for each vertex instance, relabeling the in-vertices and the out-vertices to their respective hub vertices for each vertex instance by a parallel binary search and updating edges affected by the relabeling, and assembling relabeled edges and vertices.