Information cartography: creating zoomable, large-scale maps of information

  • Authors:

  • Dafna Shahaf;Jaewon Yang;Caroline Suen;Jeff Jacobs;Heidi Wang;Jure Leskovec

  • Affiliations:

  • Stanford, Stanford, USA;Stanford, Stanford, USA;Stanford, Stanford, USA;Stanford, Stanford, USA;Stanford, Stanford, USA;Stanford, Stanford, USA

  • Venue:
  • Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
  • Year:
  • 2013

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Abstract

In an era of information overload, many people struggle to make sense of complex stories, such as presidential elections or economic reforms. We propose a methodology for creating structured summaries of information, which we call zoomable metro maps. Just as cartographic maps have been relied upon for centuries to help us understand our surroundings, metro maps can help us understand the information landscape. Given large collection of news documents our proposed algorithm generates a map of connections that explicitly captures story development. As different users might be interested in different levels of granularity, the maps are zoomable, with each level of zoom showing finer details and interactions. In this paper, we formalize characteristics of good zoomable maps and formulate their construction as an optimization problem. We provide efficient, scalable methods with theoretical guarantees for generating maps. Pilot user studies over real-world datasets demonstrate that our method helps users comprehend complex stories better than prior work.