Time Aggregated Graph: A Model for Spatio-temporal Networks. George, B. & Kim, S. In Spatio-temporal Networks, of SpringerBriefs in Computer Science, pages 7–24. Springer New York, 2013. ![Time Aggregated Graph: A Model for Spatio-temporal Networks [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Spatio-temporal networks represent networks where entities have spatial attributes and the topology and parameters display time-dependence. Given the significance of such networks in critical domains such as transportation science and sensor data analysis, the importance of a model that is simple, expressive and storage efficient to represent such networks cannot be understated. The model must provide support for the design of algorithms to process frequent queries that need to be answered in the application domains. This problem is challenging due to potentially conflicting requirements of model simplicity and support for efficient algorithms. Time expanded networks which have been used to model dynamic networks employ replication of the network across time instants, resulting in high storage overhead and algorithms that are computationally expensive. Time-aggregated graphs do not replicate nodes and edges across time; rather they allow the properties of edges and nodes to be modeled as a time series. The chapter presents a description and comparison of these models.
@incollection{george_time_2013,
series = {{SpringerBriefs} in {Computer} {Science}},
title = {Time {Aggregated} {Graph}: {A} {Model} for {Spatio}-temporal {Networks}},
copyright = {©2013 The Author(s)},
isbn = {978-1-4614-4917-1 978-1-4614-4918-8},
shorttitle = {Time {Aggregated} {Graph}},
url = {http://link.springer.com/chapter/10.1007/978-1-4614-4918-8_2},
abstract = {Spatio-temporal networks represent networks where entities have spatial attributes and the topology and parameters display time-dependence. Given the significance of such networks in critical domains such as transportation science and sensor data analysis, the importance of a model that is simple, expressive and storage efficient to represent such networks cannot be understated. The model must provide support for the design of algorithms to process frequent queries that need to be answered in the application domains. This problem is challenging due to potentially conflicting requirements of model simplicity and support for efficient algorithms. Time expanded networks which have been used to model dynamic networks employ replication of the network across time instants, resulting in high storage overhead and algorithms that are computationally expensive. Time-aggregated graphs do not replicate nodes and edges across time; rather they allow the properties of edges and nodes to be modeled as a time series. The chapter presents a description and comparison of these models.},
language = {en},
urldate = {2017-01-16},
booktitle = {Spatio-temporal {Networks}},
publisher = {Springer New York},
author = {George, Betsy and Kim, Sangho},
year = {2013},
doi = {10.1007/978-1-4614-4918-8_2},
pages = {7--24},
}
Downloads: 0
{"_id":"xEDMsoBodhZQbCPEn","bibbaseid":"george-kim-timeaggregatedgraphamodelforspatiotemporalnetworks-2013","author_short":["George, B.","Kim, S."],"bibdata":{"bibtype":"incollection","type":"incollection","series":"SpringerBriefs in Computer Science","title":"Time Aggregated Graph: A Model for Spatio-temporal Networks","copyright":"©2013 The Author(s)","isbn":"978-1-4614-4917-1 978-1-4614-4918-8","shorttitle":"Time Aggregated Graph","url":"http://link.springer.com/chapter/10.1007/978-1-4614-4918-8_2","abstract":"Spatio-temporal networks represent networks where entities have spatial attributes and the topology and parameters display time-dependence. Given the significance of such networks in critical domains such as transportation science and sensor data analysis, the importance of a model that is simple, expressive and storage efficient to represent such networks cannot be understated. The model must provide support for the design of algorithms to process frequent queries that need to be answered in the application domains. This problem is challenging due to potentially conflicting requirements of model simplicity and support for efficient algorithms. Time expanded networks which have been used to model dynamic networks employ replication of the network across time instants, resulting in high storage overhead and algorithms that are computationally expensive. Time-aggregated graphs do not replicate nodes and edges across time; rather they allow the properties of edges and nodes to be modeled as a time series. The chapter presents a description and comparison of these models.","language":"en","urldate":"2017-01-16","booktitle":"Spatio-temporal Networks","publisher":"Springer New York","author":[{"propositions":[],"lastnames":["George"],"firstnames":["Betsy"],"suffixes":[]},{"propositions":[],"lastnames":["Kim"],"firstnames":["Sangho"],"suffixes":[]}],"year":"2013","doi":"10.1007/978-1-4614-4918-8_2","pages":"7–24","bibtex":"@incollection{george_time_2013,\n\tseries = {{SpringerBriefs} in {Computer} {Science}},\n\ttitle = {Time {Aggregated} {Graph}: {A} {Model} for {Spatio}-temporal {Networks}},\n\tcopyright = {©2013 The Author(s)},\n\tisbn = {978-1-4614-4917-1 978-1-4614-4918-8},\n\tshorttitle = {Time {Aggregated} {Graph}},\n\turl = {http://link.springer.com/chapter/10.1007/978-1-4614-4918-8_2},\n\tabstract = {Spatio-temporal networks represent networks where entities have spatial attributes and the topology and parameters display time-dependence. Given the significance of such networks in critical domains such as transportation science and sensor data analysis, the importance of a model that is simple, expressive and storage efficient to represent such networks cannot be understated. The model must provide support for the design of algorithms to process frequent queries that need to be answered in the application domains. This problem is challenging due to potentially conflicting requirements of model simplicity and support for efficient algorithms. Time expanded networks which have been used to model dynamic networks employ replication of the network across time instants, resulting in high storage overhead and algorithms that are computationally expensive. Time-aggregated graphs do not replicate nodes and edges across time; rather they allow the properties of edges and nodes to be modeled as a time series. The chapter presents a description and comparison of these models.},\n\tlanguage = {en},\n\turldate = {2017-01-16},\n\tbooktitle = {Spatio-temporal {Networks}},\n\tpublisher = {Springer New York},\n\tauthor = {George, Betsy and Kim, Sangho},\n\tyear = {2013},\n\tdoi = {10.1007/978-1-4614-4918-8_2},\n\tpages = {7--24},\n}\n\n","author_short":["George, B.","Kim, S."],"key":"george_time_2013-1","id":"george_time_2013-1","bibbaseid":"george-kim-timeaggregatedgraphamodelforspatiotemporalnetworks-2013","role":"author","urls":{"Paper":"http://link.springer.com/chapter/10.1007/978-1-4614-4918-8_2"},"metadata":{"authorlinks":{}},"html":""},"bibtype":"incollection","biburl":"https://bibbase.org/zotero/wybert","dataSources":["TJkbwzD8s2wCxBy6Y"],"keywords":[],"search_terms":["time","aggregated","graph","model","spatio","temporal","networks","george","kim"],"title":"Time Aggregated Graph: A Model for Spatio-temporal Networks","year":2013}