1. A computer-implemented method, comprising: receiving one or more user-specified attribute labels of a user node and a set of probability distributions associated with possible attribute labels of nodes directly connected from the user node from a social graph in a social networking system; establishing multiple functions with variable likelihood scores of possible attribute labels of the user node and the set of the probability distributions as parameters; generating multiple equations representing explainability scores respectively corresponding to the nodes based on the multiple functions and the probability distributions associated with the nodes, each explainability score measuring a likelihood that a direct social connection between a directly connected node (“v”) and the user node is explained by at least a shared attribute label between the user node and the directly connected node; and ranking a first set of the possible attribute labels for the user node by maximizing the explainability scores utilizing the multiple equations respectively associated with the nodes, the explainability scores being implicit predictions of formation reasons of social connections between each of the nodes and the user node.
1. A computer-implemented method, comprising: receiving one or more user-specified attribute labels of a user node and a set of probability distributions associated with possible attribute labels of nodes directly connected from the user node from a social graph in a social networking system; establishing multiple functions with variable likelihood scores of possible attribute labels of the user node and the set of the probability distributions as parameters; generating multiple equations representing explainability scores respectively corresponding to the nodes based on the multiple functions and the probability distributions associated with the nodes, each explainability score measuring a likelihood that a direct social connection between a directly connected node (“v”) and the user node is explained by at least a shared attribute label between the user node and the directly connected node; and ranking a first set of the possible attribute labels for the user node by maximizing the explainability scores utilizing the multiple equations respectively associated with the nodes, the explainability scores being implicit predictions of formation reasons of social connections between each of the nodes and the user node. 3. The computer-implemented method of claim 1 , wherein the multiple functions correspond to multinomial distributions for the user node, each multinomial distribution denoting probabilities over a type-specific subset of the possible attribute labels for a label type (“t”) of the user node, wherein the multinomial distributions are established to be consistent with the user-specified attribute labels of the user node.