1. A computer-implemented method, comprising: identifying a first data set and a second data set, establishing a plurality of relationships, each of the plurality of relationships linking an element of the first data set to an element of the second data set, identifying a set of categories for categorizing elements of the first and second data sets, initializing a plurality of first ranking vector, each of the first ranking vectors corresponding to an element of the first data set, each component of the each first ranking vector a representing a degree to which the element is related to one of the categories, initializing a plurality of second ranking vector, each of the second ranking vectors corresponding to an element of the second data set, each component of the each second ranking vector u representing a degree to which the element is related to one of the categories, assigning a link weight to each of the plurality of relationships of interconnected elements of the first and second data sets, said link weight being modifiable by interaction between the elements linked by the each relationship, computing a similarity quotient for the each relationship of the plurality of relationships from a formula: 1 - 1 2 ⁢ ∑ j = 0 M - 1 ⁢  a j - u j  wherein a j and u j are components of the first and second ranking vectors a and u, respectively, and the first and second ranking vectors a and u are M-dimensional and normalized; and classifying the plurality of relationships based on values of the similarity quotients.