Abstract: A computer-implemented method, according to one embodiment, includes: generating two or more sample graphs by sampling edges of a current snapshot of a dynamic graph, generating two or more partial results by executing an algorithm on the two or more sample graphs, combining the partial results into a final result, and incrementally maintaining the sample graphs. Edges included in the current snapshot of a dynamic graph and which were added to the dynamic graph in a most recent update thereto are included in each of the generated two or more sample graphs. Moreover, incrementally maintaining the sample graphs includes: subsampling each of the edges of each of the sample graphs at a given time by applying a Bernoulli trial, and combining a result of the subsampling with new edges received in a batch corresponding to the given time to form new sample graphs.

Abstract: This disclosure is directed to large-scale graph processing to determine second-degree connections for members of a social network. A social graph is duplicated into two graphs, where each of the two graphs are partitioned into various partitions. The partitions are each sorted according to a predetermined key selected from each of the graphs. The partitions are then assigned logical Work Units, where a first set of Work Units are determined from a first graph and second set of Work Units are determined from a second graph. The Work Units are determined to be asymmetrical such that the partitions of the first set of Work Units are assigned differently than the partitions of the second set of Work Units. One set of Work Units are loaded in-memory and another set of Work Units are streamed to a mapping module process, which determines the second-degree connections from the sets of Work Units.

Abstract: A computer-implemented method, according to one embodiment, includes: generating two or more sample graphs by sampling edges of a current snapshot of a dynamic graph, generating two or more partial results by executing an algorithm on the two or more sample graphs, combining the partial results into a final result, and incrementally maintaining the sample graphs. Edges included in the current snapshot of a dynamic graph and which were added to the dynamic graph in a most recent update thereto are included in each of the generated two or more sample graphs. Moreover, incrementally maintaining the sample graphs includes: subsampling each of the edges of each of the sample graphs at a given time by applying a Bernoulli trial, and combining a result of the subsampling with new edges received in a batch corresponding to the given time to form new sample graphs.

Abstract: In one general embodiment, a computer-implemented method is provided for analyzing a dynamic graph. The computer-implemented method includes generating two or more sample graphs by sampling edges of a current snapshot of a dynamic graph. Additionally, the computer-implemented method includes generating two or more partial results by executing an algorithm on the sample graphs. Still yet, the computer-implemented method includes combining the partial results, from executing the algorithm on the sample graphs, into a final result.

Abstract: In one general embodiment, a computer-implemented method is provided for analyzing a dynamic graph. The computer-implemented method includes generating two or more sample graphs by sampling edges of a current snapshot of a dynamic graph. Additionally, the computer-implemented method includes generating two or more partial results by executing an algorithm on the sample graphs. Still yet, the computer-implemented method includes combining the partial results, from executing the algorithm on the sample graphs, into a final result.

Abstract: This disclosure is directed to large-scale graph processing to determine second-degree connections for members of a social network. A social graph is duplicated into two graphs, where each of the two graphs are partitioned into various partitions. The partitions are each sorted according to a predetermined key selected from each of the graphs. The partitions are then assigned logical Work Units, where a first set of Work Units are determined from a first graph and second set of Work Units are determined from a second graph. The Work Units are determined to be asymmetrical such that the partitions of the first set of Work Units are assigned differently than the partitions of the second set of Work Units. One set of Work Units are loaded in-memory and another set of Work Units are streamed to a mapping module process, which determines the second-degree connections from the sets of Work Units.