A method for observing social network propagation commences by establishing a graph of the social network, the graph having nodes and edges. Thereafter a graph prior is determined that reflects the graph's structure. A set of edge probabilities between nodes in the graph is iteratively optimized a using the graph prior, wherein each of said edge probabilities represents a probability of a first node influencing a second node.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for determining social network inferences, comprising: establishing a graph of the social network, the graph having nodes connected by edges; determining a graph prior that reflects a structure of the graph; and iteratively optimizing a set of edge probabilities between nodes in the graph using the graph prior, wherein each of said edge probabilities represents a probability of a first node influencing a second node. 2. The method of claim 1, wherein iteratively optimizing the set of edge probabilities between nodes comprises performing an alternate minimization-maximization. 3. The method of claim 2, wherein performing an alternate minimization-maximization comprises minimizing an objective function that is a sum of a convex function and a concave function. 4. The method of claim 1, wherein the graph prior depends on the l1 norm. 5. The method of claim 4, wherein the prior is of the form ∏ i ∈ V  f  (  b · i  1 ) where V is a set of nodes in the graph, f( )is a density function that depends on the l1 norm of an underlying vector b.i that represents the influence probabilities of users that influence the user i. 6. The method of claim 5, wherein the density function is strictly positive, differentiable, log-convex, and non-increasing over the real numbers. 7. The method of claim 1, wherein the prior is of the form ∏ i ∈ V   f  ( ∑ j ∈ V  \  { i }   1 1 - b ij ) where V is a set of nodes in the graph, f( )is a density function, and bij is the influence probability between a node i and a node j. 8. The method of claim 7, wherein the density function is strictly positive, differentiable, log-convex, and non-increasing over the real numbers. 9. A non-transitory computer readable storage medium comprising a computer readable program for finding the space spanned by user profiles, wherein the computer readable program when executed on a computer causes the computer to perform the steps of claim 1. 10. A system for social network inferences, comprising: a processor configured to (a) establish a graph of the social network, the graph having nodes connected by edges; (b) determine a graph prior that reflects a structure of the graph; and (c) iteratively optimize a set of edge probabilities between nodes in the graph using the graph prior, and wherein each of said edge probabilities represents a probability of a first node influencing a second node. 11. The system of claim 10, wherein the optimization module is an alternate minimization-maximization module configured to perform an alternate minimization-maximization to optimize the set of edge probabilities. 12. The system of claim 11, wherein the alternate minimization-maximization module is configured to minimize an objective function that is a sum of a convex function and a concave function. 13. The system of claim 10, wherein the graph prior depends on the l1 norm. 14. The system of claim 13, wherein the prior is of the form ∏ i ∈ V  f  (  b · i  1 ) where V is a set of nodes in the graph, f( ) is a density function that depends on the l1 norm of an underlying vector b.i that represents the influence probabilities of users that influence the user i. 15. The system of claim 14, wherein the density function is strictly positive, differentiable, log-convex, and non-increasing over the real numbers. 16. The system of claim 10, wherein the prior is of the form ∏ i ∈ V   f  ( ∑ j ∈ V  \  { i }   1 1 - b ij ) where V is a set of nodes in the graph, f( ) is a density function, and bij is the influence probability between a node i and a node j. 17. The system of claim 16, wherein the density function is strictly positive, differentiable, log-convex, and non-increasing over the real numbers.