GRAPHGPT-O: Synergistic Multimodal Comprehension and Generation on Graphs

In our current approach, we treat the graph as homogeneous, simplifying all nodes and edges into a single type. However, real-world graphs often consist of multiple node and edge types, each with unique semantic meanings. Future research could address this limitation by extending GraphGPT-o to heterogeneous graphs, allowing for richer and more nuanced representations of complex structures.