Harnessing the Power of Large Language Model for Uncertainty Aware Graph Processing

arXiv:2404.00589v1 Announce Type: new
Abstract: 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 …

abstract arxiv assumptions become cs.cl cs.lg data factorization geometry graph graph data language language model large language large language model matrix power processing relations tasks type uncertainty