# HopRank: How Semantic Structure Influences Teleportation in PageRank (A Case Study on BioPortal)

## TL;DR

This paper presents HopRank, a new algorithm modeling human navigation on semantic networks by incorporating the tendency to follow nodes at certain distances, revealing how semantic structure influences user behavior in ontology exploration.

## Contribution

Introduces HopRank, a novel algorithm that accounts for users' knowledge of semantic network structure, improving understanding of navigation patterns beyond traditional PageRank assumptions.

## Key findings

- Users prefer navigating within k-hop neighborhoods.
- Semantic structure influences teleportation in PageRank.
- Model fits clickstream data on biomedical ontologies.

## Abstract

This paper introduces HopRank, an algorithm for modeling human navigation on semantic networks. HopRank leverages the assumption that users know or can see the whole structure of the network. Therefore, besides following links, they also follow nodes at certain distances (i.e., k-hop neighborhoods), and not at random as suggested by PageRank, which assumes only links are known or visible. We observe such preference towards k-hop neighborhoods on BioPortal, one of the leading repositories of biomedical ontologies on the Web. In general, users navigate within the vicinity of a concept. But they also "jump" to distant concepts less frequently. We fit our model on 11 ontologies using the transition matrix of clickstreams, and show that semantic structure can influence teleportation in PageRank. This suggests that users--to some extent--utilize knowledge about the underlying structure of ontologies, and leverage it to reach certain pieces of information. Our results help the development and improvement of user interfaces for ontology exploration.