A simple embedding for classifying networks with a few graphlets

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Abstract

Complex networks are a key analytical tool for complex systems. However if one wants to apply this tool in machine learning applications where the data is non-relational data one must find an appropriate network embedding. The embedding represents a network in a vector space, while preserving information about network structure. In this paper, we propose a simple network embedding technique that avoids the need for graph kernels or convolutional networks, as have previously been advocated. Our embedding is based on 3-node and 4-node graphlet counts combined with some feature extraction based on a Principal Component Analysis (PCA). We show that it is competitive with some state-of-the-art methods on a downstream classification task. We then show how to reduce the computational effort in the method by transforming extraction into a feature selection procedure. We claim that this selection procedure, a generalisation of PCA, is more meaningful than a popular alternative.
Original languageEnglish
Title of host publication2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
EditorsMartin Atzmuller, Michele Coscia, Rokia Missaoui
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages635-642
Number of pages8
ISBN (Electronic)9781728110561
ISBN (Print)9781728110578
DOIs
Publication statusPublished - 24 Mar 2021
Event2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining -
Duration: 7 Dec 202010 Dec 2020

Publication series

NameProceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020

Conference

Conference2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Period7/12/2010/12/20

Keywords

  • complex networks
  • graphlets
  • graph classification
  • motifs

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