LUSTC: A Novel Approach for Predicting Link States on Dynamic Attributed Networks

Predicting link states on dynamic attributed networks is one of the most fundamental problems for network analysis. To accurately predict the formations and disappearances of links, we need to utilize three types of information, that is, structural information, node content information, and temporal information, simultaneously. To this end, we propose a novel approach called LUSTC for predicting links and unlinks with structural information, temporal information, and node content information on dynamic attributed networks. For each snapshot, LUSTC first randomly chooses some sets of active nodes whose degree changes between adjacent snapshots and collects higher-order structural information using a random walk method based on the active nodes. Then, LUSTC generates two global matrices containing the temporal structural information and temporal node content information, respectively, as well as a sequence of auxiliary matrices for reconstructing the structural information and node content information of snapshots. These generated matrices are optimized using a nonnegative matrix factorization (NMF) method based on the structural information and node content information of snapshots. Finally, LUSTC estimates the similarity matrix for future snapshots to predict the links that are more likely to be formed or broken. The experimental results on various dynamic attributed networks demonstrate the effectiveness of LUSTC on the link prediction and unlink prediction tasks.