First of all, thank you very much for the great work. I am doing some data analysis on anti-money laundering and also using streamlit-agraph. As you may know, when a person is suspected of money laundering, they tend to have a huge number of transaction counterparts, which means there can be a lot of nodes in the graph (ranging from tens to thousands). What troubles me is that when there are so many nodes, the program gets stuck for a long time and the drawing becomes very slow. Apart from reducing unnecessary nodes to improve performance, are there any other ways to speed things up in terms of the program itself or other aspects?
zsz00
commented
1 year ago
First of all, thank you very much for the great work. I am doing some data analysis on anti-money laundering and also using streamlit-agraph. As you may know, when a person is suspected of money laundering, they tend to have a huge number of transaction counterparts, which means there can be a lot of nodes in the graph (ranging from tens to thousands). What troubles me is that when there are so many nodes, the program gets stuck for a long time and the drawing becomes very slow. Apart from reducing unnecessary nodes to improve performance, are there any other ways to speed things up in terms of the program itself or other aspects?