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Network-Based Unsupervised Learning

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Machine Learning in Complex Networks

Abstract

In this chapter, we present representative state-of-the-art unsupervised learning techniques that rely on networked environments to conduct the learning process. In a typical unsupervised task, no external knowledge is presented to the algorithm. As such, the learning process is guided by the provided data, since no prior knowledge about the existing groups is supplied. For network-based methods, the learning procedure is performed by navigating in networks that are constructed from the input data set according to some similarity criterion. As networks naturally embody topological information of data relationships, network-based methods take advantage over algorithms that make use of raw, vector-based data. Moreover, network-based methods can be conceived as a general solution for unsupervised learning tasks even for data sets that are not represented by networks. In this case, we can apply network formation techniques on that data set to generate a network from the input data. Once the network is constructed, all of the network-based techniques described in this chapter can effectively be employed.

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Notes

  1. 1.

    See Chap. 4 for a thorough review on network formation methods and similarity functions.

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  1. Departament of Research (Depep), Central Bank of Brazil, Brasília, Distrito Federal, Brazil

    Thiago Christiano Silva

  2. Computer Science and Mathematics, University of São Paulo (USP), Ribeirão Preto, São Paulo, Brazil

    Liang Zhao

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  1. Thiago Christiano Silva
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Silva, T.C., Zhao, L. (2016). Network-Based Unsupervised Learning. In: Machine Learning in Complex Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-17290-3_6

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