Link prediction is a crucial task in graph machine learning, where the goal is to infer missing or future links within a graph. Traditional approaches leverage heuristic methods based on widely observed connectivity patterns, offering broad applicability and generalizability without the need for model training. Despite their utility, these methods are limited by their reliance on human-derived heuristics and lack the adaptability of data-driven approaches. Conversely, parametric link predictors excel in automatically learning the connectivity patterns from data and achieving state-of-the-art but fail short to directly transfer across different graphs. Instead, it requires the cost of extensive training and hyperparameter optimization to adapt to the target graph. In this work, we introduce the Universal Link Predictor (UniLP), a novel model that combines the generalizability of heuristic approaches with the pattern learning capabilities of parametric models. UniLP is designed to autonomously identify connectivity patterns across diverse graphs, ready for immediate application to any unseen graph dataset without targeted training. We address the challenge of conflicting connectivity patterns-arising from the unique distributions of different graphs-through the implementation of In-context Learning (ICL). This approach allows UniLP to dynamically adjust to various target graphs based on contextual demonstrations, thereby avoiding negative transfer. Through rigorous experimentation, we demonstrate UniLP's effectiveness in adapting to new, unseen graphs at test time, showcasing its ability to perform comparably or even outperform parametric models that have been finetuned for specific datasets. Our findings highlight UniLP's potential to set a new standard in link prediction, combining the strengths of heuristic and parametric methods in a single, versatile framework.

翻译：链接预测是图机器学习中的关键任务，旨在推断图中缺失或未来的链接。传统方法基于广泛观测到的连接模式采用启发式方法，具有广泛的适用性和泛化能力，无需模型训练。然而，这些方法受限于依赖人工设计的启发规则，缺乏数据驱动方法的适应性。相反，参数化链接预测器能够从数据中自动学习连接模式并达到最先进性能，但无法直接跨不同图迁移，需要对目标图进行大量训练和超参数优化。本文提出通用链接预测器（UniLP），一种结合启发式方法泛化能力与参数化模型模式学习能力的新型模型。UniLP能够自主识别跨不同图的连接模式，无需针对性训练即可直接应用于任何未见过的图数据集。我们通过引入上下文学习（ICL）解决不同图独特分布引发的连接模式冲突问题——该方法使UniLP基于上下文示例动态适应各类目标图，从而避免负迁移。通过严格实验，我们验证了UniLP在测试阶段适应新未见图的有效性，其性能可媲美甚至超越针对特定数据集微调的参数化模型。研究结果表明，UniLP有望在单一通用框架中融合启发式方法与参数化方法的优势，树立链接预测新标准。