A method for predicting the behavior of an electronic social network (ESN) includes identifying one user's connections with other users and creating a data structure in a memory that represents the users and their connections in the ESN. A plurality of data sources for electronic communications between users are analyzed and assigned a relative importance value. A weight is also assigned to each of the connections between the users. The weight is an encoded value computed based on a link structure of the connections where the link structure includes metadata indicating a category and a status of the respective connection. The probability that one user will communicate with one of the other users is calculated based on the analyzed plurality of data sources calculating, and the user's connections with respect to other users are ranked based on the calculated probabilities.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, comprising: identifying, by an electronic apparatus, one user's connections with other users in an electronic social network; creating, by the electronic apparatus, a data structure in a memory that represents the one user, the other users, and the one user's connections with the other users; analyzing, by the electronic apparatus, a plurality of data sources for electronic communications between the one user and the other users including assigning a relative importance value to each data source of the plurality of data sources and assigning a weight to each of the connections between the one user and the other users, wherein the weight is an encoded value computed based on a link structure of the connections, the link structure including metadata indicating a category and a status of the respective connections, the weight enabling an emulation and behavioral prediction of the electronic social network by the electronic apparatus in response to a stimulus applied to the electronic social network; calculating, by the electronic apparatus, for each of the other users the probability that the one user will communicate with that other user based on the analyzed plurality of data sources; and ranking, by the electronic apparatus, the one user's connections with the other users based on the calculated probabilities. 2. The method of claim 1, wherein creating, by the electronic apparatus, the data structure includes creating a graph that represents the one user, the other users, and the one user's connections with the other users. 3. The method of claim 2, further comprising removing from the graph, by the electronic apparatus, one or more of the one user's connections based on the ranked one user's connections. 4. The method of claim 3, wherein removing from the graph, by the electronic apparatus, one or more of the one user's connections based on the ranked one user's connections includes removing from the graph, by the electronic apparatus, one or more of the one user's connections that are ranked lower than a predetermined threshold ranking. 5. The method of claim 1, wherein calculating, by the electronic apparatus, for each of the other users the probability that the one user will communicate with that other user based on the analyzed plurality of data sources includes calculating, by the electronic apparatus, for each of the other users the probability that the one user will communicate with that other user based on a subset of the analyzed plurality of data sources, the subset excluding one or more data sources with the relative importance value below a predetermined threshold importance value. 6. The method of claim 1, wherein analyzing, by the electronic apparatus, the plurality of data sources for electronic communications between the one user and the other users further includes weighting, by the electronic apparatus, electronic communications data from each data source of the plurality of data sources based on the assigned relative importance value assigned to each data source. 7. The method of claim 6, wherein calculating, by the electronic apparatus, for each of the other users the probability that the one user will communicate with that other user based on the analyzed plurality of data sources includes calculating, by the electronic apparatus, for each of the other users the probability that the one user will communicate with that other user based on the weighted electronic communications data from each data source. 8. A method, comprising: identifying, by an electronic apparatus, one user's connections with other users in an electronic social network; creating, by the electronic apparatus, a network graph data structure in a memory that represents the one user, the other users, and the one user's connections with the other users; analyzing, by the electronic apparatus, a plurality of data sources for electronic communications between the one user and the other users; calculating, by the electronic apparatus, for each of the other users a probability that the one user will communicate with that other user based on the analyzed plurality of data sources; ranking, by the electronic apparatus, the one user's connections with the other users based on the calculated probabilities; removing from the network graph data structure, by the electronic apparatus, one or more data structure elements of the one user's connections based on the ranked one user's connections to create a signature graph; and forming a diagnostic model of the electronic social network, the diagnostic model including the signature graph to accelerate a processing speed of the diagnostic model on a social network analysis system. 9. The method of claim 8, wherein removing from the network graph data structure, by the electronic apparatus, one or more of the one user's connections based on the ranked one user's connections includes removing from the network graph data structure, by the electronic apparatus, one or more of the one user's connections that are ranked lower than a predetermined threshold ranking. 10. The method of claim 8, wherein: analyzing, by the electronic apparatus, the plurality of data sources for electronic communications between the one user and the other users includes assigning a relative importance value to each data source of the plurality of data sources; and wherein calculating, by the electronic apparatus, for each of the other users the probability that the one user will communicate with that other user based on the analyzed plurality of data sources includes calculating, by the electronic apparatus, for each of the other users the probability that the one user will communicate with that other user based on a subset of the analyzed plurality of data sources, the subset excluding one or more data sources with the relative importance value below a predetermined threshold importance value. 11. The method of claim 8, wherein analyzing, by the electronic apparatus, the plurality of data sources for electronic communications between the one user and the other users further includes weighting, by the electronic apparatus, electronic communications data from each data source of the plurality of data sources based on the assigned relative importance value assigned to each data source. 12. The method of claim 11, wherein calculating, by the electronic apparatus, for each of the other users the probability that the one user will communicate with that other user based on the analyzed plurality of data sources includes calculating, by the electronic apparatus, for each of the other users the probability that the one user will communicate with that other user based on the weighted electronic communications data from each data source. 13. A method, comprising: creating, by an electronic apparatus, a diagnostic model of an electronic social network, the diagnostic model including a data structure stored in a memory that represents one user's connections with other users in the electronic social network; analyzing, by the electronic apparatus, a plurality of data sources for electronic communications between the one user and the other users; computing and assigning, by the electronic apparatus, a relative importance value to each data source of the plurality of data sources; accelerating a processing speed of the diagnostic model to provide a near-real-time response by removing, by the electronic apparatus, one or more data sources defined at or below a specified threshold; and applying a stimulus, by the electronic apparatus, to the diagnostic model to analyze a potential response in the electronic social network based on the stimulus. 14. The method of claim 13, further comprising assigning, by the electronic apparatus, a weight to each connection between the one user and the other users, wherein the weight is an encoded value computed based on a link structure of the connection, the link structure including metadata indicating a category and a status of the connection. 15. The method of claim 13, further comprising: calculating, by the electronic apparatus, for each of the other users the probability that the one user will communicate with that other user based on the analyzed plurality of data sources; and ranking, by the electronic apparatus, the one user's connections with the other users based on the calculated probabilities. 16. The method of claim 15, further comprising accelerating the processing speed of the diagnostic model to provide the near-real-time response by removing, by the electronic apparatus, one or more connections based on the rankings.