Microsoft Technology Licensing, LLC

Heng Zhang

Benyu Zhang

Teresa Mah

Jeremy Tantrum

Jian Wang

Zheng Chen

Yi Zhang

Ying Li

Hua-Jun Zeng

Dou Shen

Abhinai Srivastava

Valeri Liborski

Dong Zhuang

Sahil Thaker

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

Systems, methods, and computer storage media having computer-executable instructions embodied thereon that facilitate advertisement selection using advertising units. An entity that is a sequence of two or more words is referenced. The entity includes substrings comprising a portion of the entity. Search data in association with the entity is compared to corresponding search data in association with the substrings of the entity. Based on the comparison, it is determined that the entity comprises an advertisable unit that functions as a unit for purposes of selecting an advertisement for display. The advertisable unit is used to select an advertisement to be presented to the user.

Facilitating advertisement selection using advertisable units

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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Bellevue, WA, United States of America

Heng Zhang