Temporal graphs are ubiquitous in real-world scenarios, such as social network, trade and transportation. Predicting dynamic links between nodes in a temporal graph is of vital importance. Traditional methods usually leverage the temporal neighborhood of interaction history to generate node embeddings first and then aggregate the source and target node embeddings to predict the link. However, such methods focus on learning individual node representations, but overlook the pairwise representation learning nature of link prediction and fail to capture the important pairwise features of links such as common neighbors (CN). Motivated by the success of Neural Common Neighbor (NCN) for static graph link prediction, we propose TNCN, a temporal version of NCN for link prediction in temporal graphs. TNCN dynamically updates a temporal neighbor dictionary for each node, and utilizes multi-hop common neighbors between the source and target node to learn a more effective pairwise representation. We validate our model on five large-scale real-world datasets from the Temporal Graph Benchmark (TGB), and find that it achieves new state-of-the-art performance on three of them. Additionally, TNCN demonstrates excellent scalability on large datasets, outperforming popular GNN baselines by up to 6.4 times in speed. Our code is available at https: //github.com/GraphPKU/TNCN.

我们提出了TNCN，这是NCN的一个时间版本，用于预测时间图中的链接，并使用多跳公共邻居来学习更有效的成对表示。我们在五个大规模真实世界数据集上验证了我们的模型，并发现它在其中三个上实现了最新的最先进表现。此外，TNCN在处理大型数据集上表现出了出色的可扩展性，在速度上超过了流行的GNN基线最多6.4倍。

高效神经常见邻居用于时间图链接预测