Supervised Nested PageRank

Graph-based ranking plays a key role in many applications, such as web search and social computing. Pioneering methods of ranking on graphs (e.g., PageRank and HITS) computed ranking scores relying only on the graph structure. Recently proposed methods, such as Semi-Supervised Page-Rank, take into account both the graph structure and the metadata associated with nodes and edges in a unified optimization framework. Such approaches are based on initializing the underlying random walk models with prior weights of nodes and edges that in turn depend on their individual properties. While in those models the prior weights of nodes and edges depend only on their own features, one can also assume that such weights may also depend or be related to the prior weights of their neighbors. This paper addresses the problem of weighting nodes and edges according to this intuition by realizing it in a general ranking model and an efficient algorithm of tuning the parameters of that model.