Network Capacity Bound for Personalized PageRank in Multimodal Networks
This work addresses theoretical limits in personalized ranking for multimodal networks, but it appears incremental as it builds directly on prior results.
The authors extended the concept of Bipartite PageRank to multimodal networks using hypergraphs, introducing a generalized PageRank and random walk model, and proved theorems on authority outflow limits for cases with identical and distinct damping factors.
In a former paper the concept of Bipartite PageRank was introduced and a theorem on the limit of authority flowing between nodes for personalized PageRank has been generalized. In this paper we want to extend those results to multimodal networks. In particular we deal with a hypergraph type that may be used for describing multimodal network where a hyperlink connects nodes from each of the modalities. We introduce a generalisation of PageRank for such graphs and define the respective random walk model that can be used for computations. We state and prove theorems on the limit of outflow of authority for cases where individual modalities have identical and distinct damping factors.