Mapping structural diversity in networks sharing a given degree distribution and global clustering: Adaptive resolution grid search evolution with Diophantine equation-based mutations

Overbury, Peter, Kiss, István Z and Berthouze, Luc (2018) Mapping structural diversity in networks sharing a given degree distribution and global clustering: Adaptive resolution grid search evolution with Diophantine equation-based mutations. Complex Networks 2018: The 7th International Conference on Complex Networks and Their Applications, Cambridge, United Kingdom, December 11-13, 2018. Published in: Cherifi, C B, Cherifi, H, Karsai, M and Musolesi, M, (eds.) Complex Networks & Their Applications VII - Proceedings The 7th International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2018. 812 718-730. Springer ISSN 1860-949X ISBN 9783030054106

[img] PDF - Accepted Version
Restricted to SRO admin only until 2 December 2019.

Download (647kB)

Abstract

Methods that generate networks sharing a given degree distribution and global clustering can induce changes in structural properties other than that controlled for. Diversity in structural properties, in turn, can affect the outcomes of dynamical processes operating on those networks. Since exhaustive sampling is not possible, we propose a novel evolutionary framework for mapping this structural diversity. The three main features of this framework are: (a) subgraph-based encoding of networks, (b) exact mutations based on solving systems of Diophantine equations, and (c) heuristic diversity-driven mechanism to drive resolution changes in the MapElite algorithm.We show that our framework can elicit networks with diversity in their higher-order structure and that this diversity affects the behaviour of the complex contagion model. Through a comparison with state of the art clustered network generation methods, we demonstrate that our approach can uncover a comparably diverse range of networks without needing computationally unfeasible mixing times. Further, we suggest that the subgraph-based encoding provides greater confidence in the diversity of higher-order network structure for low numbers of samples and is the basis for explaining our results with complex contagion model. We believe that this framework could be applied to other complex landscapes that cannot be practically mapped via exhaustive sampling.

Item Type: Conference Proceedings
Schools and Departments: School of Engineering and Informatics > Informatics
School of Mathematical and Physical Sciences > Mathematics
Research Centres and Groups: Centre for Computational Neuroscience and Robotics
Evolutionary and Adaptive Systems Research Group
Sussex Neuroscience
Subjects: Q Science > QA Mathematics > QA0075 Electronic computers. Computer science
Related URLs:
Depositing User: Luc Berthouze
Date Deposited: 19 Oct 2018 14:08
Last Modified: 07 Dec 2018 14:04
URI: http://srodev.sussex.ac.uk/id/eprint/79597

View download statistics for this item

📧 Request an update