I am working on an algorithm that ranks a set of nodes in a graph with respect to how relative this node is to other predefined nodes (I call them query nodes). The way how the algorithm works is similar to recommendation algorithms. For instance, if i want to buy an item from an online store, the algorithm will look at my preferences (and/or history of purchased items) and recommend new items for me. Applying this to graph theory, the set of nodes are items and my preferred items are the query nodes. The problem am facing right now is how to benchmark my results (i.e. I want to run recall and precision on my results) but I don't have a ground truth data. My question is: does anyone know a benchmark for this problem, if not, how do you think I can evaluate my results.
Note: My algorithm has nothing to do with recommendation algorithms (i.e. the application is different), but I gave this to deliver the general idea of the RELATIVE IMPORTANCE algorithms. I am looking for any dataset with benchmark that may help me in this context.
Edit: Based on some requests, I will explain my algorithm with more details. The algorithm takes as input: graph (can be directed or undirected, weighted or unweighted), and a set of query nodes (included in the graph). The algorithm will try to rank the nodes in the graph according to their importance with respect to the query nodes. The importance of a node increases as the relationship between it and the query nodes increases. Depending on the application, this relationship is quantified by a value (the weight of an edge) that reflects the level of association between two nodes. For instance, in the DBLP co-authership dataset, the relation between two nodes is the number of common papers between the two nodes (authors). Therefore, in this case, the algorithm will rank the authors in the DBLP graph according to how close they are to all query nodes (the predefined authors). I hope that this is clear.