Hello. I have created a graph model on Tigergraph Cloud and mapped the data successfully. My question is how do I present the full graph diagram instead of the graph schema on the GraphStudio?
@huxu If I’m understanding your question correctly you would like to visualize all the data in your graph in Graph Studio? There is a limitation on the browser (hardware/software constraints) on nodes that can be shown, but you can do that with the query from this post (below). Let me know if that helps!
https://dev.tigergraph.com/forum/t/tip-1-introduction-and-show-the-whole-graph/56/5
Hey @huxu When you create a graph model on TigerGraph Cloud, you’re defining the structure of your graph data, like different types of items and how they’re related. While the default view in GraphStudio shows this structure (schema), it might not give a clear picture of your actual data relationships. To see the real connections, you can use the “Explore Graph” option. This tool lets you build queries that fetch specific data from your graph and displays it visually. You’ll see nodes for items and lines between them for connections. This way, you’ll get a clearer understanding of how your data is linked together, beyond just the schema.
By utilizing the “Explore Graph” feature, you’ll gain insights into your data’s relationships in a more visual and interactive manner.
I hope this will help you
OK. At present, I know that I can use the “Explore Graph” option, but I don’t know how to directly display all nodes and their connection relationships.
@huxu actually you can do it with query but displaying the entire graph with all nodes and edges directly through a single query might not be a straightforward task due to potential complexity and performance concerns. The primary purpose of GraphStudio is to visualize specific data based on queries you create.However, if you still want to attempt displaying all nodes and edges using a query-based approach, you might consider using a traversal query that iterates through all nodes and their connections. Keep in mind that this might not be suitable for graphs with a large number of nodes and edges, as it could lead to performance issues.