1. A method implemented in a computer system for discovering a relationship between entities comprising: receiving, at said computer system, input data W representing a word vector matrix and input data G representing a link graph matrix having values that capture relationships amongst user or business entities; computing a current log likelihood of the input data W and G, the current likelihood of the input data being a probability distribution function of topic modeling parameters and community modeling parameters, the topic modeling parameters representing topic similarity between unstructured texts of the entities and the community modeling parameters represent community similarity between the entities comparing the current log likelihood of the input data and a previous log likelihood of the input data computed previously; updating values of the parameters, if the current log likelihood is larger than the previous log likelihood; repeating the comparing and the updating until the current log likelihood becomes less than or equal to the previous log likelihood; and constructing at least one graph based on the updated values of the parameters when the current log likelihood is less than or equal to the previous log likelihood, the at least one graph indicating the relationship between the entities, wherein a program using a processor unit executes one or more of said computing comparing, updating, and constructing.