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Algorithms and Computer Resources

Algorithms and Computer Resources. Vlado Batagelj, Bill Richards, Jonathon Cummings, Michael Welge, Christian Steglich, Bruce Herr, Mengxiao Zhu, Bethany Wotal. Size of networks. Small – some hundreds of vertices Large – fits in the computer memory Huge – needs external memory.

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Algorithms and Computer Resources

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  1. Algorithms and Computer Resources Vlado Batagelj, Bill Richards, Jonathon Cummings, Michael Welge, Christian Steglich, Bruce Herr, Mengxiao Zhu, Bethany Wotal

  2. Size of networks • Small – some hundreds of vertices • Large – fits in the computer memory • Huge – needs external memory Large and huge networks are becoming reality Network = Graph + Data

  3. Decomposition

  4. Algorithms • Subquadratic algorithms for large and huge networks • Algorithms considering both network structure and data • Algorithms for multi-relational temporal networks • New approaches to classical problems (Statistical modeling can do only few relations on some 100 vertices; KEDs)

  5. INSNA • INSNA web page already provides basic support for SNA community http://www.insna.org

  6. Visualization Visualization can be used for networks of ‘moderate’ size, not too dense. The goal is to get insight into network structure. SNA deals also with multi-relational, temporal and often large networks. The 'standard' sheet of paper paradigm is often not appropriate for the amount of information in such networks. We should develop dynamic interactive layouts, introduce new visualization elements to represent typical network substructures, ... and add some artistic touch to final displays. Visualization of dense (parts of) networks – matrix display. There are many new results in graph drawing community.

  7. Network analysis algorithms There are several SNA programs Ucinet, Netminer, Pajek, Multinet, ... There is no general program for huge networks. Interoperability on the data level. Because of great diversity of potential users (needs, IT competencies, ...)it seemsthatthe 'MS Office'-like approach would provide the right answer: • (open source) library of very efficient algorithms that can be used through an API in different programming languages; • programming language above the library (some existing language canbe used/extended: Python, Javascript, R, ...); • GUI interface(s) to the library for non-programmers and specialuser-groups (for example, genealogists). The experiences with the development of statistical environment R show that ascriptinglanguage can activate a number of users to contribute (packages) to the development ofthe system - thus becoming an integration platform for the field.

  8. Adoption issues • Recognition to the authors • Support for development of open solutions

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