Microsoft Technology Licensing, LLC

Linkedin Corporation

Qiang Zhu

Keqing Liang

Matthew Steven Tague

Peter Hume Rigano

Qingbo Hu

John Chao

Rebecca Page White

John Brendan Browne

Lorenzo Canlas

Techniques are provided for determining predicted results for entities based on relatedness of the entities in a graph of nodes. In an embodiment, the graph of nodes is generated based on the determined relatedness of the entities. A node in the graph of nodes represents an entity, and nodes representing entities with known results are assigned those results as their respective node values. The assigned node values are then propagated between the neighboring nodes throughout the graph of nodes in the amount determined by the relatedness of the nodes. Based on the propagation, node values for entities with unknown results are determined and represent the predicted results for those entities. Additionally, various classifiers may be combined with the propagated node values to increase the accuracy of the predicted results.

Graph based techniques for predicting results

In an example embodiment, a supervised machine learning algorithm is used to train a communication reply score model based on an extracted first set of features and second set of features from social networking service member profiles and activity and usage information. When a plurality of member search results is to be displayed, for the member identified in each of the plurality of member search results, the member profile corresponding to the member is parsed to extract a third set of one or more features from the member profile, activity and usage information pertaining to actions taken by the members on the social networking service is parsed to extract a fourth set of one or more features, and the extracted third set of features and fourth set of features is inputted into the communication reply score model to generate a communication reply score, which is displayed visually to a searcher.

Graph framework using heterogeneous social networks

Systems and methods for improving search results using social routing are provided. In example embodiments, an affinity metric and match metric are determined for a plurality of users. The affinity metric indicates an interaction level between a given user and the match metric indicates how well the given user matches parameters of a search string. Based on a correlation between the affinity metric and the match metric, one or more items of content may be transmitted to one or more selected users. Improved search results can then be generated based on interactions with the one or more items of content.

Search results using social routing of content

Member communication reply score calculation

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Sunnyvale, CA, United States of America

Qiang Zhu