document: Social influence analysis in large-scale networks In large social networks , nodes ( users , entities ) are influenced by others for various reasons . For example , the colleagues have strong influence on one 's work , while the friends have strong influence on one 's daily life . How to differentiate the social influences from different angles ( topics ) ? How to quantify the strength of those social influences ? How to estimate the model on real large networks ? To address these fundamental questions , we propose Topical Affinity Propagation ( TAP ) to model the topic-level social influence on large networks . In particular , TAP can take results of any topic modeling and the existing network structure to perform topic-level influence propagation . With the help of the influence analysis , we present several important applications on real data sets such as 1 ) what are the representative nodes on a given topic ? 2 ) how to identify the social influences of neighboring nodes on a particular node ? To scale to real large networks , TAP is designed with efficient distributed learning algorithms that is implemented and tested under the Map-Reduce framework . We further present the common characteristics of distributed learning algorithms for Map-Reduce . Finally , we demonstrate the effectiveness and efficiency of TAP on real large data sets .
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