Design Requirements for Human-Centered Graph Neural Network Explanations

arXiv:2405.06917v1 Announce Type: new
Abstract: Graph neural networks (GNNs) are powerful graph-based machine-learning models that are popular in various domains, e.g., social media, transportation, and drug discovery. However, owing to complex data representations, GNNs do not easily allow for human-intelligible explanations of their predictions, which can decrease trust in them as well as deter any collaboration opportunities between the AI expert and non-technical, domain expert. Here, we first discuss the two papers that aim to provide GNN explanations to domain …

abstract arxiv cs.hc cs.lg data design discovery domains drug discovery gnns graph graph-based graph neural network graph neural networks however human machine media network networks neural network neural networks popular predictions requirements social social media them transportation trust type