The small-world phenomenon: an algorithmic perspective
STOC '00 Proceedings of the thirty-second annual ACM symposium on Theory of computing
Large scale analysis of web revisitation patterns
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Mining the search trails of surfing crowds: identifying relevant websites from user activity
Proceedings of the 17th international conference on World Wide Web
Getting the most out of social annotations for web page classification
Proceedings of the 9th ACM symposium on Document engineering
Studying trailfinding algorithms for enhanced web search
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Pragmatic evaluation of folksonomies
Proceedings of the 20th international conference on World wide web
Tags vs shelves: from social tagging to social classification
Proceedings of the 22nd ACM conference on Hypertext and hypermedia
One tag to bind them all: measuring term abstractness in social metadata
ESWC'11 Proceedings of the 8th extended semantic web conference on The semanic web: research and applications - Volume Part II
Building directories for social tagging systems
Proceedings of the 20th ACM international conference on Information and knowledge management
Human wayfinding in information networks
Proceedings of the 21st international conference on World Wide Web
Navigational efficiency of broad vs. narrow folksonomies
Proceedings of the 23rd ACM conference on Hypertext and social media
Evaluating tag-based information access in image collections
Proceedings of the 23rd ACM conference on Hypertext and social media
Evaluation of Folksonomy Induction Algorithms
ACM Transactions on Intelligent Systems and Technology (TIST)
Models of human navigation in information networks based on decentralized search
Proceedings of the 24th ACM Conference on Hypertext and Social Media
The last click: why users give up information network navigation
Proceedings of the 7th ACM international conference on Web search and data mining
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Decentralized search in networks is an activity that is often performed in online tasks. It refers to situations where a user has no global knowledge of a network's topology, but only local knowledge. On Wikipedia for instance, humans typically have local knowledge of the links emanating from a given Wikipedia article, but no global knowledge of the entire Wikipedia graph. This makes the task of navigation to a target Wikipedia article from a given starting article an interesting problem for both humans and algorithms. As we know from previous studies, people can have very efficient decentralized search procedures that find shortest paths in many cases, using intuitions about a given network. These intuitions can be modeled as hierarchical background knowledge that people access to approximate a networks' topology. In this paper, we explore the differences and similarities between decentralized search that utilizes hierarchical background knowledge and actual human navigation in information networks. For that purpose we perform a large scale study on the Wikipedia information network with over 500,000 users and 1,500,000 click trails. As our results reveal, a decentralized search procedure based on hierarchies created directly from the link structure of the information network simulates human navigational behavior better than simulations based on hierarchies that are created from external knowledge.