Position-Aware Neighbor Aggregation for Dynamic Link Prediction

Dynamic link prediction, which aims to predict future interactions based on historical temporal graph evolution, plays a pivotal role in applications such as social networks, recommendation systems, and patent trend prediction. However, existing research predominantly validates model performance under a single data distribution characterized by high-density repeated interactions, which leads to an insufficient exploration of higher-order node relationships. In contrast, we argue that dynamic graphs exhibit diverse application potential, where the occurrence of future interactions is not exclusively determined by historical repeated events, but rather by higher-order positional dependencies. To fill this gap, we classify the data into two scenarios with dense and sparse repeated interactions, and encode the temporal positions of neighbors relative to the event center to better guide hierarchical neighborhood aggregation. Building upon this, we propose PANet, a position-aware dynamic graph network that captures the temporal positioning of nodes in evolving interactions. Firstly, we define the spatial and temporal position of neighbors based on their distance to the event center and their occurrence frequency. Secondly, we employ a hierarchical paired-position encoding approach to compute the temporal position representation for each neighbor. Last, we leverage the relative positions and timestamps of neighbors to guide the aggregation of neighborhood information, and derive the final node representation through an attention-based network. Experiments conducted on eight datasets across two distributions validate the effectiveness and efficiency of the proposed method. Moreover, the significant performance improvement achieved by incorporating the position-aware neighbor aggregation into existing dynamic graph methods further demonstrates its versatility.