Constant state of change: engagement inequality in temporal dynamic networks
Abstract The temporal changes in complex systems of interactions have excited the research community in recent years as they encompass understandings on their dynamics and evolution. From the collective dynamics of organizations and online communities to the spreading of information and fake news, t...
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doaj-8871baf4bfcb467799c4e6a06c4ea3a02020-11-25T03:32:44ZengSpringerOpenApplied Network Science2364-82282019-10-014111410.1007/s41109-019-0181-9Constant state of change: engagement inequality in temporal dynamic networksHadar Miller0Osnat Mokryn1Information and Knowledge Management, University of HaifaInformation and Knowledge Management, University of HaifaAbstract The temporal changes in complex systems of interactions have excited the research community in recent years as they encompass understandings on their dynamics and evolution. From the collective dynamics of organizations and online communities to the spreading of information and fake news, to name a few, temporal dynamics are fundamental in the understanding of complex systems. In this work, we quantify the level of engagement in dynamic complex systems of interactions, modeled as networks. We focus on interaction networks for which the dynamics of the interactions are coupled with that of the topology, such as online messaging, forums, and emails. We define two indices to capture the temporal level of engagement: the Temporal Network (edge) Intensity index, and the Temporal Dominance Inequality index. Our surprising results are that these measures are stationary for most measured networks, regardless of vast fluctuations in the size of the networks in time. Moreover, more than 80% of weekly changes in the indices values are bounded by less than 10%. The indices are stable between the temporal evolution of a network but are different between networks, and a classifier can determine the network the temporal indices belong to with high success. We find an exception in the Enron management email exchange during the year before its disintegration, in which both indices show high volatility throughout the inspected period.http://link.springer.com/article/10.1007/s41109-019-0181-9Engagement indicesInteractions intensityDominanceGini-index inequality |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hadar Miller Osnat Mokryn |
spellingShingle |
Hadar Miller Osnat Mokryn Constant state of change: engagement inequality in temporal dynamic networks Applied Network Science Engagement indices Interactions intensity Dominance Gini-index inequality |
author_facet |
Hadar Miller Osnat Mokryn |
author_sort |
Hadar Miller |
title |
Constant state of change: engagement inequality in temporal dynamic networks |
title_short |
Constant state of change: engagement inequality in temporal dynamic networks |
title_full |
Constant state of change: engagement inequality in temporal dynamic networks |
title_fullStr |
Constant state of change: engagement inequality in temporal dynamic networks |
title_full_unstemmed |
Constant state of change: engagement inequality in temporal dynamic networks |
title_sort |
constant state of change: engagement inequality in temporal dynamic networks |
publisher |
SpringerOpen |
series |
Applied Network Science |
issn |
2364-8228 |
publishDate |
2019-10-01 |
description |
Abstract The temporal changes in complex systems of interactions have excited the research community in recent years as they encompass understandings on their dynamics and evolution. From the collective dynamics of organizations and online communities to the spreading of information and fake news, to name a few, temporal dynamics are fundamental in the understanding of complex systems. In this work, we quantify the level of engagement in dynamic complex systems of interactions, modeled as networks. We focus on interaction networks for which the dynamics of the interactions are coupled with that of the topology, such as online messaging, forums, and emails. We define two indices to capture the temporal level of engagement: the Temporal Network (edge) Intensity index, and the Temporal Dominance Inequality index. Our surprising results are that these measures are stationary for most measured networks, regardless of vast fluctuations in the size of the networks in time. Moreover, more than 80% of weekly changes in the indices values are bounded by less than 10%. The indices are stable between the temporal evolution of a network but are different between networks, and a classifier can determine the network the temporal indices belong to with high success. We find an exception in the Enron management email exchange during the year before its disintegration, in which both indices show high volatility throughout the inspected period. |
topic |
Engagement indices Interactions intensity Dominance Gini-index inequality |
url |
http://link.springer.com/article/10.1007/s41109-019-0181-9 |
work_keys_str_mv |
AT hadarmiller constantstateofchangeengagementinequalityintemporaldynamicnetworks AT osnatmokryn constantstateofchangeengagementinequalityintemporaldynamicnetworks |
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