Microsoft Technology Licensing, LLC

Jeremy Tantrum

Benyu Zhang

Teresa Mah

Heng Zhang

Jian Wang

Zheng Chen

Hua-Jun Zeng

Abhinai Srivastava

Dong Zhuang

An influential persons identification system and method for identifying a set of influential persons (or influencers) in a social network (such as an online social network). The influential persons set is generated such that by sending a message to the set the message will be propagated through the network at the greatest speed and coverage. A ranking of users is generated, and a pruning process is performed starting with the top-ranked user and working down the list. For each user on the list, the user is identified as an influencer and then the user and each of his friends are deleted from the social network users list. Next, the same process is performed for the second-ranked user, the third-ranked user, and so forth. The process terminates when the list of users of the social network is exhausted or the desired number of influencers on the influential person set is reached.

Identifying influential persons in a social network

Methods and computer storage media for clustering nodes are provided. An input file is received that is comprised of primary nodes, secondary nodes and metrics that relate to the association between the primary nodes and the secondary nodes. Upon receiving the input file, the input file is abridged to reduce the number of nodes contained in the input file. The unique initial primary nodes are then clustered with their associated secondary node. The clusters containing the unique initial primary nodes are replaced if a subsequent related cluster satisfies a pre-defined condition. In some embodiments, multiple clusters are then merged until the cluster size reaches a pre-defined size. In some embodiments, the input file is cleaned and sorted prior to being abridged.

Efficient method for clustering nodes

A method and system are provided for identifying groups in large-scale networks. The large-scale networks include a collection of nodes and edges that may represent relationships between entities or individuals. The large-scale network is split into a number of fractions satisfying an edge threshold. In turn, the nodes in each fraction are merged to generate one or more clusters based on a specified similarity metric. The large-scale network is recursively split and clustered until distinct groups are identified.

Group identification in large-scaled networks via hierarchical clustering through refraction over edges of networks

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Shoreline, WA, United States of America

Jeremy Tantrum