Data Infrastructure and Method for Estimating Influence Spread in Social Networks

A computing system for determining influence spread in social networks, the system comprising:

• a central processor system, a multi-processor platform, and a global memory;
• the central processor system configured to generate a plurality of samples, each sample corresponding to a collection of all edge weights for a social network graph topology;
• the central processor system configured to allocate the plurality of samples into at least one batch, a size of which is being determined according to a number of threads and global memory space available in the multi-processor platform;
• for each batch:
• the multi-processor platform configured to parallel process the samples in that batch to generate results corresponding to a spread of each graph node per sample in that batch;
• and
• the global memory configured to store results of that batch, the global memory accessible to the multi-processor platform;
• the central processor system configured to compute an average spread of each node across all samples in all batches;
• and
• the central processor system configured to determine, from the average spreads, one or more nodes having a largest spread.

One or more non-transitory computer readable mediums for determining influence spread in social networks, the one or more non-transitory computer readable mediums collectively comprising computer executable instructions, that when executed, cause a computing system to at least:

• generate a plurality of samples, each sample corresponding to a collection of all edge weights for a social network graph topology;
• allocate the plurality of samples into at least one batch, a size of which is being determined according to a number of threads and global memory space available in a multi-processor platform;
• for each batch:
• parallel process the samples in that batch using the multi-processor platform to generate results corresponding to a spread of each graph node per sample in that batch;
• store results of that batch in the global memory accessible to the multi-processor platform;
• and
• compute an average spread of each node across all samples in all batches;
• and
• determine, from the average spreads, one or more nodes having a largest spread.