Inductive Link Prediction in Static and Temporal Graphs for Isolated Nodes

Link prediction for low-degree and isolated nodes remains an open challenge in graph machine learning, where most models overfit to topological neighborhoods. This paper proposes inductive link prediction methods that generalize to isolated nodes in both static and temporal graph settings. We leverage unsupervised pre-training on large corpora to generate rich node representations independent of graph structure, enabling accurate predictions even for newly introduced nodes with no observed edges. Experiments across multiple benchmark datasets confirm substantial improvements in inductive generalization over existing baselines.