MuseGraph: Graph-oriented Instruction Tuning of Large Language Models for Generic Graph Mining

arXiv:2403.04780v1 Announce Type: new
Abstract: Graphs with abundant attributes are essential in modeling interconnected entities and improving predictions in various real-world applications. Traditional Graph Neural Networks (GNNs), which are commonly used for modeling attributed graphs, need to be re-trained every time when applied to different graph tasks and datasets. Although the emergence of Large Language Models (LLMs) has introduced a new paradigm in natural language processing, the generative potential of LLMs in graph mining remains largely under-explored. To this end, …

abstract applications arxiv cs.ai cs.cl every gnns graph graph mining graph neural networks graphs language language models large language large language models mining modeling networks neural networks predictions tasks type world