Abstract: A method for distributed processing involves receiving a graph (G) of targets and of influencers, with each influencer related to at least one target, receiving an action graph of actions performed by one or more of the influencers, and key partitioning G across shards. The method further involves transposing the first graph (G) to obtain a first transposed graph (GT), value partitioning GT across the shards, storing the action graph on multiple shards, issuing, to a shard, a request specifying an influencer, to perform an intersection, receiving a response to the request of a set of influencers each of which is related to a target, and determining whether to send a recommendation to the target based on the response.

Abstract: A method for distributed processing involves receiving a graph (G) of targets and of influencers, with each influencer related to at least one target, receiving an action graph of actions performed by one or more of the influencers, and key partitioning G across shards. The method further involves transposing the first graph (G) to obtain a first transposed graph (GT), value partitioning GT across the shards, storing the action graph on multiple shards, issuing, to a shard, a request specifying an influencer, to perform an intersection, receiving a response to the request of a set of influencers each of which is related to a target, and determining whether to send a recommendation to the target based on the response.

Abstract: A method for distributed processing involves receiving a graph (G) of targets and of influencers, with each influencer related to at least one target, receiving an action graph of actions performed by one or more of the influencers, and key partitioning G across shards. The method further involves transposing the first graph (G) to obtain a first transposed graph (GT), valuing partitioning GT across the shards, storing the action graph on multiple shards, issuing, to a shard, a request specifying an influencer, to perform an intersection, receiving a response to the request of a set of influencers each of which is related to a target, and determining whether to send a recommendation to the target based on the response.

Abstract: A method for distributed processing involves receiving a graph (G) of targets and of influencers, with each influencer related to at least one target, receiving an action graph of actions performed by one or more of the influencers, and key partitioning G across shards. The method further involves transposing the first graph (G) to obtain a first transposed graph (GT), valuing partitioning GT across the shards, storing the action graph on multiple shards, issuing, to a shard, a request specifying an influencer, to perform an intersection, receiving a response to the request of a set of influencers each of which is related to a target, and determining whether to send a recommendation to the target based on the response.

Abstract: A method for distributed processing involves receiving a graph (G) of targets and of influencers, with each influencer related to at least one target, receiving an action graph of actions performed by one or more of the influencers, and key partitioning G across shards. The method further involves transposing the first graph (G) to obtain a first transposed graph (GT), valuing partitioning GT across the shards, storing the action graph on multiple shards, issuing, to a shard, a request specifying an influencer, to perform an intersection, receiving a response to the request of a set of influencers each of which is related to a target, and determining whether to send a recommendation to the target based on the response.

Abstract: A method for distributed processing involves receiving a graph (G) of targets and of influencers, with each influencer related to at least one target, receiving an action graph of actions performed by one or more of the influencers, and key partitioning G across shards. The method further involves transposing the first graph (G) to obtain a first transposed graph (GT), valuing partitioning GT across the shards, storing the action graph on multiple shards, issuing, to a shard, a request specifying an influencer, to perform an intersection, receiving a response to the request of a set of influencers each of which is related to a target, and determining whether to send a recommendation to the target based on the response.

Abstract: A method for distributed processing involves receiving a graph (G) of targets and of influencers, with each influencer related to at least one target, receiving an action graph of actions performed by one or more of the influencers, and key partitioning G across shards. The method further involves transposing the first graph (G) to obtain a first transposed graph (GT), valuing partitioning GT across the shards, storing the action graph on multiple shards, issuing, to a shard, a request specifying an influencer, to perform an intersection, receiving a response to the request of a set of influencers each of which is related to a target, and determining whether to send a recommendation to the target based on the response.