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。