基于时空轨迹的多视点匹配网络社会关系推理
Social Link Inference via Multi-View Matching Network from Spatio-Temporal Trajectories
摘要：
在本文中，我们研究的问题，社会链接推理的目标位置感知社会网络（LSN），其目的是预测网络内的用户之间的未观察到的联系。这个问题对于包括网络补全和好友推荐在内的下游应用至关重要。除了在一般链接预测中常用的网络结构之外，针对LSN中的社会链接推断量身定制的研究从空间方面利用用户轨迹。然而，大多数先前的研究在很大程度上忽略了用户轨迹中的时间因素，限制了捕捉用户之间的时间相关性的能力。此外，通过融合不同的视图，即，社会，空间和时间因素，仍然没有得到解决，这阻碍了潜在的改进链接推理。为此，本文设计了一种新的多视图匹配网络（MVMN），将这三个因素中的每一个视为任何目标用户对的一个视图。MVMN通过开发合适的匹配模块，实现了对每个因素建模的灵活性和完整性：1）位置匹配模块，2）时间序列匹配模块，3）关系匹配模块。每个模块学习一个特定于视图的表示进行匹配，MVMN将它们融合在一起进行最终的链接推理。在两个真实数据集上的大量实验证明了我们的方法对链接预测和序列匹配的几个竞争基线的优越性，验证了其关键组件的贡献。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
