Efficient Partial Order Preserving Unsupervised Feature Selection on Networks

In the past decade, research on network data has attracted much attention and many interesting phenomena have been discovered. Such data are often characterized by high dimensionality but how to select meaningful and more succinct features for network data received relatively less attention. In this paper, we investigate unsupervised feature selection problem on networks. To effectively incorporate linkage information, we propose a Partial Order Preserving (POP) principle for evaluating features. We show the advantage of this novel formulation in several respects: effectiveness, efficiency and its connection to optimizing AUC. We propose three instantiations derived from the POP principle and evaluate them using three real-world datasets. Experimental results show that our approach has significantly better performance than state-of-the-art methods under several different metrics.