时间链接预测器需要学习记忆吗？一个仅含少量参数的平滑计数基线

中文摘要 AI 辅助

许多时间链接预测器通过学习节点表示来总结过去的交互。我们考察了简单的重复交互模式计数是否能在不学习这些表示的情况下提供具有竞争力的预测。我们提出了一种基于统计语言建模的时间链接预测器。它汇集跨来源的转移和共现计数，以预测某个来源从未形成的链接。我们使用目标频率或Kneser-Ney延续计数对稀疏估计进行平滑处理。一个共享的对数线性规则将这些估计与流行度、来源历史和近期性相结合，而不使用节点嵌入。在我们的主要评估中，该模型在TGB和TGB-Seq的16个数据集中有7个数据集上取得了所比较方法中最高的MRR。它还在所有16个数据集上优于EdgeBank和Base3，并在14个数据集上优于启发式方法族。这些优势也扩展到为限制重复边而设计的数据集。仅使用9至13个学习参数，我们的模型为评估未来的神经时间链接预测器提供了一个简单且具有竞争力的基线。

英文摘要

Many temporal link predictors summarize past interactions through learned node representations. We examine whether simple counts of recurring interaction patterns can provide competitive predictions without learning these representations. We propose a temporal link predictor based on statistical language modelling. It pools transition and co-occurrence counts across sources to predict links that a source has never formed. We smooth sparse estimates using destination frequencies or Kneser-Ney continuation counts. A shared log-linear rule combines these estimates with popularity, source history, and recency, without node embeddings. In our main evaluation, the model achieves the highest MRR among the compared methods on 7 out of 16 datasets from TGB and TGB-Seq. It also outperforms EdgeBank and Base3 on all 16 datasets and the heuristic family on 14. These gains extend to datasets designed to limit repeated edges. With only 9--13 learned parameters, our model provides a simple and competitive baseline for evaluating future neural temporal link predictors.

发表机构

• Julius-Maximilians-Universität Würzburg（尤利乌斯-马克西米利安-维尔茨堡大学）

机构由 AI 辅助整理，请以论文原文为准。