  Large Language Models (LLMs) are increasingly used for various tasks with
graph structures. Though LLMs can process graph information in a textual
format, they overlook the rich vision modality, which is an intuitive way for
humans to comprehend structural information and conduct general graph
reasoning. The potential benefits and capabilities of representing graph
structures as visual images (i.e., $\textit{visual graph}$) are still
unexplored. To fill the gap, we innovatively propose an end-to-end framework,
called $\textbf{G}$raph to v$\textbf{I}$sual and $\textbf{T}$extual
Integr$\textbf{A}$tion (GITA), which firstly incorporates visual graphs into
general graph reasoning. Besides, we establish $\textbf{G}$raph-based
$\textbf{V}$ision-$\textbf{L}$anguage $\textbf{Q}$uestion $\textbf{A}$nswering
(GVLQA) dataset from existing graph data, which is the first vision-language
dataset for general graph reasoning purposes. Extensive experiments on the
GVLQA dataset and five real-world datasets show that GITA outperforms
mainstream LLMs in terms of general graph reasoning capabilities. Moreover, We
highlight the effectiveness of the layout augmentation on visual graphs and
pretraining on the GVLQA dataset.
