@inproceedings{5ee4ac2a416a4d67ad3041c127d8157c,
title = "A simple embedding for classifying networks with a few graphlets",
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.",
keywords = "complex networks, graphlets, graph classification, motifs",
author = "{le Gorrec}, Luce and Knight, {Philip A.}",
year = "2021",
month = mar,
day = "24",
doi = "10.1109/ASONAM49781.2020.9381337",
language = "English",
isbn = "9781728110578",
series = "Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020",
publisher = "IEEE",
pages = "635--642",
editor = "Martin Atzmuller and Michele Coscia and Rokia Missaoui",
booktitle = "2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)",
note = "2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining ; Conference date: 07-12-2020 Through 10-12-2020",
}