Social Link Inference via Multi-View Matching Network from Spatio-Temporal Trajectories

TL;DR

This paper introduces a multi-view matching network that integrates spatial, temporal, and social data to improve social link prediction in location-aware social networks, addressing limitations of prior methods that overlook temporal factors.

Contribution

The paper proposes a novel multi-view matching network (MVMN) that effectively fuses spatial, temporal, and social views for more accurate social link inference.

Findings

MVMN outperforms baseline methods on real-world datasets.

Incorporating temporal information enhances link prediction accuracy.

Each view-specific module contributes significantly to overall performance.

Abstract

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.