GraphControl: Adding Conditional Control to Universal Graph Pre-trained Models for Graph Domain Transfer Learning

arXiv:2310.07365v3 Announce Type: replace
Abstract: Graph-structured data is ubiquitous in the world which models complex relationships between objects, enabling various Web applications. Daily influxes of unlabeled graph data on the Web offer immense potential for these applications. Graph self-supervised algorithms have achieved significant success in acquiring generic knowledge from abundant unlabeled graph data. These pre-trained models can be applied to various downstream Web applications, saving training time and improving downstream (target) performance. However, different graphs, even across seemingly similar domains, …

abstract algorithms applications arxiv control cs.lg daily data domain enabling graph graph data objects pre-trained models relationships structured data success transfer transfer learning type universal web world