  Handling graph data is one of the most difficult tasks. Traditional
techniques, such as those based on geometry and matrix factorization, rely on
assumptions about the data relations that become inadequate when handling large
and complex graph data. On the other hand, deep learning approaches demonstrate
promising results in handling large graph data, but they often fall short of
providing interpretable explanations. To equip the graph processing with both
high accuracy and explainability, we introduce a novel approach that harnesses
the power of a large language model (LLM), enhanced by an uncertainty-aware
module to provide a confidence score on the generated answer. We experiment
with our approach on two graph processing tasks: few-shot knowledge graph
completion and graph classification. Our results demonstrate that through
parameter efficient fine-tuning, the LLM surpasses state-of-the-art algorithms
by a substantial margin across ten diverse benchmark datasets. Moreover, to
address the challenge of explainability, we propose an uncertainty estimation
based on perturbation, along with a calibration scheme to quantify the
confidence scores of the generated answers. Our confidence measure achieves an
AUC of 0.8 or higher on seven out of the ten datasets in predicting the
correctness of the answer generated by LLM.
