  In this paper, we investigate the problem of social link inference in a
target Location-aware Social Network (LSN), which aims at predicting the
unobserved links between users within the network. This problem is critical for
downstream applications including network completion and friend recommendation.
In addition to the network structures commonly used in general link prediction,
the studies tailored for social link inference in an LSN leverage user
trajectories from the spatial aspect. However, the temporal factor lying in
user trajectories is largely overlooked by most of the prior studies, limiting
the capabilities of capturing the temporal relevance between users. Moreover,
effective user matching by fusing different views, i.e., social, spatial, and
temporal factors, remains unresolved, which hinders the potential improvement
of link inference. To this end, this paper devises a novel multi-view matching
network (MVMN) by regarding each of the three factors as one view of any target
user pair. MVMN enjoys the flexibility and completeness of modeling each factor
by developing its suitable matching module: 1) location matching module, 2)
time-series matching module, and 3) relation matching module. Each module
learns a view-specific representation for matching, and MVMN fuses them for
final link inference. Extensive experiments on two real-world datasets
demonstrate the superiority of our approach against several competitive
baselines for link prediction and sequence matching, validating the
contribution of its key components.
