1. A system, comprising: a memory having computer readable computer instructions; and a processor for executing the computer readable instructions to perform a method comprising: accessing a graph comprised of a plurality of nodes and edges, each node representing a topic, and each edge representing a known association between two topics; determining a first probability that, given that an agent has expressed an interest in a single topic represented by a node in the graph, the agent is interested in each of the topics represented by nodes in the graph, the determining comprising: performing a statistical traversal experiment on said graph, the performing including using a generalized form of a matrix eigenvector algorithm that includes a Markov chain specialized to the first topic; inferring a strength of relations between the agent and each of the topics represented by nodes in the graph, the inferring based on statistics extracted from the statistical traversal experiment; and adjusting the inferred strength of relations to account for interests expressed in each of the topics by other agents in a reference population; deriving a second probability that, given that the agent has expressed an interest in two or more topics represented by nodes in the graph, the agent is interested in each of the topics represented by nodes in the graph, calculating an estimate probability by combining the first probability and the second probability using a log-likelihood ratios, wherein lack of interest expressed by the agent in each of the topics is represented as subtraction using the log-likelihood ratios.
1. A system, comprising: a memory having computer readable computer instructions; and a processor for executing the computer readable instructions to perform a method comprising: accessing a graph comprised of a plurality of nodes and edges, each node representing a topic, and each edge representing a known association between two topics; determining a first probability that, given that an agent has expressed an interest in a single topic represented by a node in the graph, the agent is interested in each of the topics represented by nodes in the graph, the determining comprising: performing a statistical traversal experiment on said graph, the performing including using a generalized form of a matrix eigenvector algorithm that includes a Markov chain specialized to the first topic; inferring a strength of relations between the agent and each of the topics represented by nodes in the graph, the inferring based on statistics extracted from the statistical traversal experiment; and adjusting the inferred strength of relations to account for interests expressed in each of the topics by other agents in a reference population; deriving a second probability that, given that the agent has expressed an interest in two or more topics represented by nodes in the graph, the agent is interested in each of the topics represented by nodes in the graph, calculating an estimate probability by combining the first probability and the second probability using a log-likelihood ratios, wherein lack of interest expressed by the agent in each of the topics is represented as subtraction using the log-likelihood ratios. 5. The system of claim 1 , wherein the graph is a sparse graph.