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.

本研究介绍了一种名为 UniLP 的全能连接预测模型，它结合了启发式方法的普适性和参数化模型的模式学习能力，能够自动识别不同图表的连接模式并在无需针对性训练的情况下迅速应用于任何未知图表数据集。该模型通过 In-context Learning (ICL) 的实现解决了不同图表之间独特分布导致的连接模式冲突问题，通过顺应上下文演示动态调整到各种目标图表，避免了负面迁移。通过严格的实验证明了 UniLP 在适应新的未知图表上的有效性，并展示了它与为特定数据集进行微调的参数化模型性能相当甚至胜过的能力。我们的发现突出了 UniLP 在连接预测领域中将启发式方法和参数化方法的优势结合于一个多用途框架中的潜力。