I apologize for lack of terminology, I'm no computer scientist. I have a problem of validating paths in a directed graph with complex nodes.
The full description is the following:
- I have a decent set (about 1K) of directed graphs;
- Each node contains a complex data structure (it is a hierarchical data structure, not a picture or sound);
- I have some of paths in those graphs known as "correct" paths (based mostly on data in nodes);
- And I have some of paths in those graphs known as "incorrect" paths (with classification why it is incorrect).
I'd like to predict given a graph with those complex nodes and a path, is this path "correct".
Which machine learning algorithm will suit me best? In general, what approach I should use?
- Each full graph is either have app paths processed (correct/incorrect) or completely blank (no path is processed);
- Correctness depends on both position of node in a graph AND data in the node;
- Humans would need heuristics to decide or guess which paths are correct;
- Most of the paths are "correct";
- I hope to convert human heuristics to some kind of "correctness" recognition.