  Understanding and characterizing the process deriving the creation and
dissolution of social interactions is a fundamental challenge for social
network analysis. In the dynamic setting, it is essential to be able to, given
the collection of link states of the network in a certain period, accurately
predict the link and unlink states of the network in a future time. Addressing
this task is more complicated compared to its static counterpart especially for
increasingly large networks due to the prohibitive expensiveness of
computational complexity. Consequently, mainstreams of current researches in
unsupervised settings ignore the temporal information. Additionally, only a few
approaches study on unlink prediction, which is also important to understand
the evolution of social networks.
  In this work, we address such mining tasks by unsupervised learning, and
propose a model for link and unlink prediction with temporal information
(LUPT). Given a sequence of snapshots of network over time, LUPT utilizes the
spectral diffusion by variants of local random walks to calculate the
probability vector started from each node at each snapshot. Then, it calculates
the similarity score for each of the nodes by the probability vectors of all
the previous snapshots. Finally, LUPT predicts the link and unlink states by
ranking the similarity scores according to the link and unlink tasks,
respectively. Experiments on real-world networks demonstrate that LUPT provides
superior results compared to the baseline methods in both link and unlink
prediction tasks.
