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Complex networks are structurally distinguishable by domain

Complex networks are structurally distinguishable by domain Complex networks arise in many domains and often represent phenomena such as brain activity, social relationships, molecular interactions, hyperlinks, and re-tweets. In this work, we study the problem of predicting the category (domain) of arbitrary networks. This includes complex networks from different domains as well as synthetically generated graphs from six different network models. We formulate this problem as a multiclass classification problem and learn a model to predict the domain of a new previously unseen network using only a small set of simple structural features. The model is able to accurately predict the domain of arbitrary networks from 17 different domains with 95.7% accuracy. This work makes two important findings. First, our results indicate that complex networks from various domains have distinct structural properties that allow us to predict with high accuracy the category of a new previously unseen network. Second, synthetic graphs are trivial to classify as the classification model can predict with near-certainty the graph model used to generate it. Overall, the results demonstrate that networks drawn from different domains and graph models are distinguishable using a few simple structural features. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Social Network Analysis and Mining Springer Journals

Complex networks are structurally distinguishable by domain

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References (68)

Publisher
Springer Journals
Copyright
Copyright © 2019 by Springer-Verlag GmbH Austria, part of Springer Nature
Subject
Computer Science; Data Mining and Knowledge Discovery; Applications of Graph Theory and Complex Networks; Game Theory, Economics, Social and Behav. Sciences; Statistics for Social Sciences, Humanities, Law; Methodology of the Social Sciences
ISSN
1869-5450
eISSN
1869-5469
DOI
10.1007/s13278-019-0593-7
Publisher site
See Article on Publisher Site

Abstract

Complex networks arise in many domains and often represent phenomena such as brain activity, social relationships, molecular interactions, hyperlinks, and re-tweets. In this work, we study the problem of predicting the category (domain) of arbitrary networks. This includes complex networks from different domains as well as synthetically generated graphs from six different network models. We formulate this problem as a multiclass classification problem and learn a model to predict the domain of a new previously unseen network using only a small set of simple structural features. The model is able to accurately predict the domain of arbitrary networks from 17 different domains with 95.7% accuracy. This work makes two important findings. First, our results indicate that complex networks from various domains have distinct structural properties that allow us to predict with high accuracy the category of a new previously unseen network. Second, synthetic graphs are trivial to classify as the classification model can predict with near-certainty the graph model used to generate it. Overall, the results demonstrate that networks drawn from different domains and graph models are distinguishable using a few simple structural features.

Journal

Social Network Analysis and MiningSpringer Journals

Published: Sep 19, 2019

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