Location-Aware Social Network Recommendation via Temporal Graph Networks

In the data-driven era, recommendations have become indispensable across various systems. Graphs, as versatile data structures, shine at abstracting complex systems. Many real-world scenarios effortlessly translate into graphs, representing individuals and their relationships as nodes and edges. Link prediction, a cornerstone of recommendations, excels in forecasting future network connections based on current structures. Its applications span diverse domains, including social networks, biological networks, and network security. Previous studies have leveraged classification algorithms like logistic regression and random forest, often complemented by node embedding techniques, yielding impressive results in addressing the challenge of link prediction. Today's dynamic networks continually reshape connections, introducing new links and nodes while removing others. Furthermore, the inclusion of location information associated with nodes provides a new opportunity. Adapting models to this dynamism necessitates capturing spatial and temporal dependencies for sustained effectiveness. In this paper, we undertake a comprehensive evaluation of various algorithms for link prediction. Subsequently, we further enriched the continuous-time dynamic graph networks by incorporating essential location information. This strategic enhancement results in a remarkable performance improvement, highlighting the crucial role of location-based temporal data in improving recommendations. It emphasizes the untapped potential of location and temporal information in refining user recommendations within interconnected networks.