Imperial College London
Alternate

DrTimEvans

Faculty of Natural Sciences, Department of Physics

Senior Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 7837t.evans Website

 
 
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Assistant

 

Mrs Graziela De Nadai-Sowrey +44 (0)20 7594 7843

 
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Location

 

609Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Vasiliauskaite:2020:10.1007/s41109-020-00255-5,
author = {Vasiliauskaite, V and Evans, TS},
doi = {10.1007/s41109-020-00255-5},
journal = {Applied Network Science},
pages = {1--24},
title = {Making communities show respect for order},
url = {http://dx.doi.org/10.1007/s41109-020-00255-5},
volume = {5},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - In this work we give a community detection algorithm in which the communities both respects the intrinsic order of a directed acyclic graph and also finds similar nodes. We take inspiration from classic similarity measures of bibliometrics, used to assess how similar two publications are, based on their relative citation patterns. We study the algorithm’s performance and antichain properties in artificial models and in real networks, such as citation graphs and food webs. We show how well this partitioning algorithm distinguishes and groups together nodes of the same origin (in a citation network, the origin is a topic or a research field). We make the comparison between our partitioning algorithm and standard hierarchical layering tools as well as community detection methods. We show that our algorithm produces different communities from standard layering algorithms.
AU  - Vasiliauskaite,V
AU  - Evans,TS
DO  - 10.1007/s41109-020-00255-5
EP  - 24
PY  - 2020///
SN  - 2364-8228
SP  - 1
TI  - Making communities show respect for order
T2  - Applied Network Science
UR  - http://dx.doi.org/10.1007/s41109-020-00255-5
UR  - https://appliednetsci.springeropen.com/articles/10.1007/s41109-020-00255-5
UR  - http://hdl.handle.net/10044/1/77678
VL  - 5
ER  -
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