COME: Complementary Reliability Monitoring with Expert Allocation for Multi-Source-Free Graph Domain Adaptation

This paper studies the problem of multi-source-free graph domain adaptation, which aims to adapt multiple pretrained source models to target graphs without access to source graph data. Existing approaches usually combine pseudo-labeling techniques with domain alignment to learn from unlabeled target graphs. Despite the progress, their performance can degrade when source predictions are overconfident due to overconfident pseudo-labels from unaligned source models and thus potential error accumulation. To address this problem, we propose a novel approach named Complementary Reliability Monitoring with Expert Allocation (COME) for multi-source-free graph domain adaptation. The core of our COME extracts complementary structural and temporal stability signals for target supervision and expert allocation, i.e., structural stability and temporal stability. From the structural perspective, we feed perturbed views of the target graph into source models and compare their predictions to estimate structural stability. From the temporal perspective, we train a target-adaptive model and accumulate disagreement between target predictions and memory-based pseudo-labels that characterize temporal stability. These conflict scores are further incorporated into node states to stabilize pseudo-label supervision during target model adaptation. During inference, we leverage complementary stability signals as a routing function to dynamically allocate both frozen source and trainable target experts for reliable target predictions. Experiments on three benchmark families show that COME achieves the best mean accuracy in all six transfer settings.