Network Completion: Beyond Matrix Completion

Abstract

Due to practical reasons such as limited resources and privacy settings specified by users on social media, most network data tend to be only partially observed with both missing nodes and missing edges. Thus, it is of paramount importance to infer the missing parts of the networks since incomplete network data may severely degrade the performance of downstream analyses. In this paper, we provide a comprehensive survey on network completion, which is a more challenging task than the well-studied low-rank matrix completion problem in the sense that a row and a column of an adjacency matrix shall be entirely unobservable when a node is completely missing from the given network. Specifically, we first define the problem of network completion. Then, we review two state-of-the-art algorithms for discovering the missing part of an underlying network, namely KronEM and DeepNC. We also show a performance comparison between the two algorithms via experimental evaluation. Finally, we discuss the potentials and limitations of the two algorithms.