A method, system and computer program product are disclosed for predicting influence in a social network. In one embodiment, the method comprises identifying a set of users of the social network, and identifying a subset of the users as influential users based on defined criteria. A multitude of measures are identified as predictors of which ones of the set of users are the influential users. These measures are aggregated, and a composite predictor model is formed based on this aggregation. This composite predictor model is used to predict which ones of the set of users will have a specified influence in the social network in the future. In one embodiment, the specified influence is based on messages sent from the users, and for example, may be based on the number of the messages sent from each user that are re-sent by other users.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of predicting users who will have a specified influence in a social network, comprising: identifying a set of users of the social network; identifying a subset of the users as influential users based on defined criteria; identifying a multitude of ranking measures as predictors of which ones of the set of users are said influential users; obtaining a training set of data from the set of users; using said training set of data to determine, for each of the ranking measures, an effectiveness of said each ranking measure as a predictor of which ones of the set of the users are said influential users; and forming a composite predictor model from said user ranking measures to determine which ones of the set of users will have a specified influence in the social network in the future. 2. The method according to claim 1, wherein the obtaining a training set of data includes: dividing the users of the set of users into a plurality of classes of users; and training a model on the ranking measures on historical data of the users of a selected one of the plurality of classes of users. 3. The method according to claim 2, wherein the training a model on the ranking measures includes using logistic regression to train said model. 4. The method according to claim 1, wherein: in said social network, messages are sent by and among the users of the set of users; and the using said training set of data to determine, for each of the ranking measures, an effectiveness of said each ranking measure as a predictor of which ones of the set of the users are said influential users includes using said training set of data to determine, for each of the ranking measures, an effectiveness of said each ranking measure as a predictor of which ones of the users of the set of users will have the most number of the messages sent from said ones of the users re-sent by others of the users. 5. The method according to claim 1, wherein the forming a composite predictor model from said user ranking measures includes: using each of said ranking measures to provide a respective one ranking of the influence in the social network of an associated group of users of the set of users; aggregating said respective one rankings to form an aggregate ranking of said associated group of the users; and using said aggregate ranking to predict which ones of the set of users will have a specified influence in the social network in the future. 6. The method according to claim 1, wherein the forming the composite predictor model includes using all of said multitude of ranking measures to form the composite predictor model. 7. The method according to claim 1, wherein the forming the composite predictor model includes using only some of said multitude of ranking measures to form the composite predictor model. 8. The method according to claim 1, wherein the forming the composite predictor model includes combining all of said measures through logistic regression to form the composite predictor model. 9. The method according to claim 1, wherein: the identifying a subset of the users includes separating the set of users into a multitude of classes according to degrees of influence of the users based on said defined criteria; and the using said composite predictor model includes using the composite predictor model to predict how often users in the different classes will have said specified influence in the social network in the future. 10. The method according to claim 1, wherein: the identifying a subset of the users includes separating the set of users into a multitude of classes according to a degree of influence of each of the users based on said defined criteria; and the forming a composite predictor model includes forming the composite predictor model based on an accuracy of each of the ranking measures as a predictor of which of the users are in which of the classes. 11. A system for predicting users who will have a specified influence in a social network, said social network includes an identified set of users and a subset of the users identified as influential users based on defined criteria, the system comprising one or more processing units configured for: identifying a multitude of ranking measures as predictors of which ones of the set of users are said influential users; obtaining a training set of data from the set of users; using said training set of data to determine, for each of the ranking measures, an effectiveness of said each ranking measure as a predictor of which ones of the set of the users are said influential users; and forming a composite predictor model from said user ranking measures to determine which ones of the set of users will have a specified influence in the social network in the future. 12. The system according to claim 11, wherein the obtaining a training set of data includes: dividing the users of the set of users into a plurality of classes of users; and training a model on the ranking measures on historical data of the users of a selected one of the plurality of classes of users. 13. The system according to claim 12, wherein the training a model on the ranking measures includes using logistic regression to train said model. 14. The system according to claim 11, wherein: in said social network, messages are sent by and among the users of the set of users; and the using said training set of data to determine, for each of the ranking measures, an effectiveness of said each ranking measure as a predictor of which ones of the set of the users are said influential users includes using said training set of data to determine, for each of the ranking measures, an effectiveness of said each ranking measure as a predictor of which ones of the users of the set of users will have the most number of the messages sent from said ones of the users re-sent by others of the users. 15. The system according to claim 11, wherein the forming a composite predictor model from said user ranking measures includes: using each of said ranking measures to provide a respective one ranking of the influence in the social network of an associated group of users of the set of users; aggregating said respective one rankings to form an aggregate ranking of said associated group of the users; and using said aggregate ranking to predict which ones of the set of users will have a specified influence in the social network in the future. 16. An article of manufacture comprising: at least one tangible computer readable hardware medium having computer readable program code logic tangibly embodied therein to predict users who will have a specified influence in a social network, and wherein said social network includes an identified set of users and a subset of the set of users identified as influential users based on defined criteria, and a multitude of ranking measures are identified as predictors of which ones of the set of users are said influential users, said computer readable program code logic, when executing, performing the following: obtaining a training set of data from the set of users; using said training set of data to determine, for each of the ranking measures, an effectiveness of said each ranking measure as a predictor of which ones of the set of the users are said influential users; and forming a composite predictor model from said user ranking measures to determine which ones of the set of users will have a specified influence in the social network in the future. 17. The article of manufacture according to claim 16, wherein the obtaining a training set of data includes: dividing the users of the set of users into a plurality of classes of users; and training a model on the ranking measures on historical data of the users of a selected one of the plurality of classes of users. 18. The article of manufacture according to claim 17, wherein the training a model on the ranking measures includes using logistic regression to train said model. 19. The article of manufacture according to claim 16, wherein: in said social network, messages are sent by and among the users of the set of users; and the using said training set of data to determine, for each of the ranking measures, an effectiveness of said each ranking measure as a predictor of which ones of the set of the users are said influential users includes using said training set of data to determine, for each of the ranking measures, an effectiveness of said each ranking measure as a predictor of which ones of the users of the set of users will have the most number of the messages sent from said ones of the users re-sent by others of the users. 20. The article of manufacture according to claim 17, wherein the forming a composite predictor model from said user ranking measures includes: using each of said ranking measures to provide a respective one ranking of the influence in the social network of an associated group of users of the set of users; aggregating said respective one rankings to form an aggregate ranking of said associated group of the users; and using said aggregate ranking to predict which ones of the set of users will have a specified influence in the social network in the future.