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Network Analysis

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Introduction to Data Science

Part of the book series: Undergraduate Topics in Computer Science ((UTICS))

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Abstract

Network data are currently generated and collected to an increasing extent from different fields. In this chapter, we show how network data analysis allows us to gain insight into the data that would be hard to acquire by other means. We introduce some tools in network analysis and visualization. We present important concepts such as connected components, centrality measures, and ego-networks, as well as community detection. We use a Python toolbox (NetworkX) to build graphs easily and analyze them. We motivate concepts in network analysis by a real problem dealing with a Facebook network dataset and answering a set of questions. For instance: Which is the most representative member of the network in terms of the most “connected”, the most “circulated”, the “closest” or the most “accessible” to the rest of the members?

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Notes

  1. 1.

    https://networkit.iti.kit.edu.

  2. 2.

    https://snap.stanford.edu/data/egonets-Facebook.html.

  3. 3.

    http://snap.stanford.edu/data/.

  4. 4.

    https://d3js.org.

  5. 5.

    http://perso.crans.org/aynaud/communities/.

References

  1. N. Friedkin, Structural bases of interpersonal influence in groups: A Longitudinal Case Study. American Sociological Review 58(6):861 1993

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  3. V. D. Blondel, J.-L. Guillaume, R. Lambiotte, R. Lefebvre, Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment. 2008(10)

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Acknowledgements

This chapter was co-written by Laura Igual and Santi Seguí.

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Correspondence to Laura Igual .

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© 2017 Springer International Publishing Switzerland

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Igual, L., Seguí, S. (2017). Network Analysis. In: Introduction to Data Science. Undergraduate Topics in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-50017-1_8

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  • DOI: https://doi.org/10.1007/978-3-319-50017-1_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50016-4

  • Online ISBN: 978-3-319-50017-1

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