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Algorithms and Tools Relevant for the Mapping of Science and HPS

Breakout session on. Algorithms and Tools Relevant for the Mapping of Science and HPS. Lead: Kevin Boyack Participants: Tony Beavers Yunwei Chen Jean-Gabriel Ganascia Jaimie Murdock Andrew Ravenscroft Chris Sula John Walsh Angela Zoss. General Considerations.

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Algorithms and Tools Relevant for the Mapping of Science and HPS

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  1. Breakout session on Algorithms and Tools Relevant for the Mapping of Science and HPS Lead: Kevin Boyack Participants: Tony BeaversYunwei ChenJean-Gabriel GanasciaJaimie MurdockAndrew RavenscroftChris SulaJohn WalshAngela Zoss

  2. General Considerations • Differences between network creation analysis and visualization algorithms • Data formats don’t fit directly into tools: for example, data needs to be fielded for some tools (may need some middleware, additional conversion tools) • What are the similarities and differences between science and HPS? Are there different needs? Can the same algorithms be applied to all data sets?

  3. Associated Challenges and Opportunities • How to generate maps/networks from HPS data?

  4. Proposed Solutions • Host a NWB demo – online workshop, DVD • Have HPS researchers send feedback on desired features to NWB team

  5. Data • TEI-XML docs (1990s); see tei-c.org • Names • Dates • Links • Text blocks (documents, without tags)

  6. Tools • NWB • Simile Timeline • Google Maps • Adobe Flex (with Google Code Library Birdeye) • Python/PERL/SQL • Pajek

  7. Algorithms • Visualization algorithms: • Layout algorithms (spring weights – no clustering) • Clustering algorithms (after/within visualization) • Treemaps • Graph layouts (nodes and edges) • Dendrogram • Self-organizing map (also generates the network) • Network creation/analysis algorithms: • TFIDF (for word co-occurrence) • Clustering algorithms (agglomerative, pure, trained/supervised)

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