  Multimodal machine learning has been widely studied for the development of
general intelligence. Recently, the remarkable multimodal algorithms, the
Perceiver and Perceiver IO, show competitive results for diverse dataset
domains and tasks. However, recent works, Perceiver and Perceiver IO, have
focused on heterogeneous modalities, including image, text, and speech, and
there are few research works for graph structured datasets. A graph is one of
the most generalized dataset structures, and we can represent the other
dataset, including images, text, and speech, as graph structured data. A graph
has an adjacency matrix different from other dataset domains such as text and
image, and it is not trivial to handle the topological information, relational
information, and canonical positional information. In this study, we provide a
Graph Perceiver IO, the Perceiver IO for the graph structured dataset. We keep
the main structure of the Graph Perceiver IO as the Perceiver IO because the
Perceiver IO already handles the diverse dataset well, except for the graph
structured dataset. The Graph Perceiver IO is a general method, and it can
handle diverse datasets such as graph structured data as well as text and
images. Comparing the graph neural networks, the Graph Perceiver IO requires a
lower complexity, and it can incorporate the local and global information
efficiently. We show that Graph Perceiver IO shows competitive results for
diverse graph-related tasks, including node classification, graph
classification, and link prediction.
