An Iterative Learning Algorithm for Within-Network Regression in the Transductive Setting

Within-network regression addresses the task of regression in partially labeled networked data where labels are sparse and continuous. Data for inference consist of entities associated with nodes for which labels are known and interlinked with nodes for which labels must be estimated. The premise of this work is that many networked datasets are characterized by a form of autocorrelation where values of the response variable in a node depend on values of the predictor variables of interlinked nodes. This autocorrelation is a violation of the independence assumption of observation. To overcome to this problem, the lagged predictor variables are added to the regression model. We investigate a computational solution for this problem in the transductive setting, which asks for predicting the response values only for unlabeled nodes of the network. The neighborhood relation is computed on the basis of the node links. We propose a regression inference procedure that is based on a co-training ...

• Type: Conference
• Year: 2009
• Where: DIS
• Authors: Annalisa Appice, Michelangelo Ceci, Donato Malerba
• Keywords: DIS 2009, Interlinked Nodes, Labels, Theoretical Computer Science, Unlabeled Nodes