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Human navigation in networks

Published: 25 June 2012 Publication History

Abstract

World around us interconnected in giant networks and we are daily navigating and finding paths through such networks. For example, we browse the Web [2], search for connections among friends in social networks, follow leads in citation networks[6, 3], of scientific literature, and look up things in cross-referenced dictionaries and encyclopedias. Even though navigating networks is an essential part of our everyday lives, little is known about the mechanisms humans use to navigate networks as well as the properties of networks that allow for efficient navigation.
We conduct two large scale studies of human navigation in networks. First, we present a study an instance of Milgram's small-world experiment where the task is to navigate from a given source to a given target node using only the local network information [5]. We perform a computational analysis of a planetary-scale social network of 240 million people and 1.3 billion edges and investigate the importance of geographic cues for navigating the network. Second, we also discuss a large-scale study of human wayfinding, in which, given a network of links between the concepts of Wikipedia, people play a game of finding a short path from a given start to a given target concept by following hyperlinks (Figure 1) [7]. We study more than 30,000 goal-directed human search paths through Wikipedia network and identify strategies people use when navigating information spaces.
Even though the domains of social and information networks are very different, we find many commonalities in navigation of the two networks. Humans tend to be good at finding short paths, despite the fact that the networks are very large [8]. Human paths differ from shortest paths in characteristic ways. At the early stages of the search navigating to a high-degree hub node helps, while in the later stage, content features and geography provide the most important clues. We also observe a trade-off between simplicity and efficiency: conceptually simple solutions are more common but tend to be less efficient than more complex ones [9].
One potential reason for good human performance could be that humans possess vast amounts of background knowledge about the network, which they leverage to make good guesses about possible paths. So we ask the question: Are human-like high-level reasoning skills really necessary for finding short paths? To answer this question, we design a number of navigation agents without such skills, which use only simple numerical features [8]. We evaluate the agents on the task of navigating both networks. We observe that the agents find shorter paths than humans on average and therefore conclude that, perhaps surprisingly, no sophisticated background knowledge or high-level reasoning is required for navigating a complex network.

References

[1]
L. Adamic and E. Adar. How to search a social network. Social Networks, 27(3):187--203, 2005.
[2]
E. H. Chi, P. Pirolli, K. Chen, and J. Pitkow. Using information scent to model user information needs and actions and the web. In Proceedings of the SIGCHI conference on Human factors in computing systems, CHI '01, pages 490--497, 2001.
[3]
P. S. Dodds, R. Muhamad, and D. J. Watts. An experimental study of search in global social networks. Science, 301(5634):827, 2003.
[4]
J. M. Kleinberg. Navigation in a small world. Nature, 406(6798):845--845, 2000.
[5]
J. Leskovec and E. Horvitz. Planetary-scale views on a large instant-messaging network. In Proceedings of the 17th international conference on World Wide Web, WWW '08, pages 915--924, 2008.
[6]
S. Milgram. The small-world problem. Psychology Today, 2(1):60--67, 1967.
[7]
R. West. Wikispeedia. Website, 2009. http://www.wikispeedia.net.
[8]
R. West and J. Leskovec. Automatic versus human navigation in information networks. In Proceedings of the AAAI International Conference on Weblogs and Social Media, ICWSM '12, 2012.
[9]
R. West and J. Leskovec. Human wayfinding in information networks. In Proceedings of the 21st International Conference on the World Wide Web, WWW '11, 2012.

Cited By

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  • (2019)“Set of Strings” Framework for Big Data ModelingOpen Source Programming for Data Science and Machine Learning [Working Title]10.5772/intechopen.85602Online publication date: 27-Apr-2019
  • (2019)Potential gain as a centrality measureIEEE/WIC/ACM International Conference on Web Intelligence10.1145/3350546.3352559(418-422)Online publication date: 14-Oct-2019
  • (2015)Combining eye tracking and pupillary dilation analysis to identify Website Key ObjectsNeurocomputing10.1016/j.neucom.2015.05.108168:C(179-189)Online publication date: 30-Nov-2015

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cover image ACM Conferences
HT '12: Proceedings of the 23rd ACM conference on Hypertext and social media
June 2012
340 pages
ISBN:9781450313353
DOI:10.1145/2309996

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 June 2012

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Author Tags

  1. decentralized search
  2. information networks
  3. navigation
  4. small-world

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HT '12
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HT '12: 23rd ACM Conference on Hypertext and Social Media
June 25 - 28, 2012
Wisconsin, Milwaukee, USA

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HT '12 Paper Acceptance Rate 33 of 120 submissions, 28%;
Overall Acceptance Rate 378 of 1,158 submissions, 33%

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Cited By

View all
  • (2019)“Set of Strings” Framework for Big Data ModelingOpen Source Programming for Data Science and Machine Learning [Working Title]10.5772/intechopen.85602Online publication date: 27-Apr-2019
  • (2019)Potential gain as a centrality measureIEEE/WIC/ACM International Conference on Web Intelligence10.1145/3350546.3352559(418-422)Online publication date: 14-Oct-2019
  • (2015)Combining eye tracking and pupillary dilation analysis to identify Website Key ObjectsNeurocomputing10.1016/j.neucom.2015.05.108168:C(179-189)Online publication date: 30-Nov-2015

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