Universal Link Predictor By In-Context Learning on Graphs
CoRR(2024)
摘要
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.
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