A tunable graph model for incorporating geographic spread in social graph models

Modeling and understanding social network structure has interested researchers from many backgrounds including social science, computer science, theoretical physics and graph theory. Notable models include [1] and [2] achieving graphs with power-law degree distribution using preferential attachment...

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Main Authors: Sharma, Rajesh, Datta, Anwitaman
Other Authors: School of Computer Engineering
Format: Conference or Workshop Item
Language:English
Published: 2013
Online Access:https://hdl.handle.net/10356/98708
http://hdl.handle.net/10220/12672
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-987082020-05-28T07:41:34Z A tunable graph model for incorporating geographic spread in social graph models Sharma, Rajesh Datta, Anwitaman School of Computer Engineering International Conference on Advances in Social Networks Analysis and Mining (2012 : Istanbul, Turkey) Modeling and understanding social network structure has interested researchers from many backgrounds including social science, computer science, theoretical physics and graph theory. Notable models include [1] and [2] achieving graphs with power-law degree distribution using preferential attachment and small-world characteristics using randomized rewiring of a regular ring lattice respectively. In contrast to a body of follow-up research which refine upon these seminal works to better capture the graph structure and characteristics (such as improving clustering coefficient by considering social triads along with preferential attachment [3]), this work aims additionally to model the geographic spread in social networks. With increased mobility in our society as well as enhanced communication opportunities social networks are increasingly spread all over the globe. Synthetic graphs imitating real-world social network characteristics are often used for driving simulations for planning and decision support. Incorporating geographic spread can facilitate better infrastructure provisioning in distributed systems supporting social and collaborative applications or model information of malware diffusion, word-of-mouth marketing, etc. The proposed model is tunable and modular. The model can be tuned to produce graphs with different geographic spread. The model is modular in the sense that existing geographic spread agnostic social network models can be plugged into our model to achieve desirable geographic spread in addition to other characteristics (such as degree distribution, clustering coefficient) that such a model would natively support. 2013-07-31T08:57:50Z 2019-12-06T19:58:42Z 2013-07-31T08:57:50Z 2019-12-06T19:58:42Z 2012 2012 Conference Paper Sharma, R., & Datta, A. (2012). A Tunable Graph Model for Incorporating Geographic Spread in Social Graph Models. 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 294-301. https://hdl.handle.net/10356/98708 http://hdl.handle.net/10220/12672 10.1109/ASONAM.2012.57 en
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
description Modeling and understanding social network structure has interested researchers from many backgrounds including social science, computer science, theoretical physics and graph theory. Notable models include [1] and [2] achieving graphs with power-law degree distribution using preferential attachment and small-world characteristics using randomized rewiring of a regular ring lattice respectively. In contrast to a body of follow-up research which refine upon these seminal works to better capture the graph structure and characteristics (such as improving clustering coefficient by considering social triads along with preferential attachment [3]), this work aims additionally to model the geographic spread in social networks. With increased mobility in our society as well as enhanced communication opportunities social networks are increasingly spread all over the globe. Synthetic graphs imitating real-world social network characteristics are often used for driving simulations for planning and decision support. Incorporating geographic spread can facilitate better infrastructure provisioning in distributed systems supporting social and collaborative applications or model information of malware diffusion, word-of-mouth marketing, etc. The proposed model is tunable and modular. The model can be tuned to produce graphs with different geographic spread. The model is modular in the sense that existing geographic spread agnostic social network models can be plugged into our model to achieve desirable geographic spread in addition to other characteristics (such as degree distribution, clustering coefficient) that such a model would natively support.
author2 School of Computer Engineering
author_facet School of Computer Engineering
Sharma, Rajesh
Datta, Anwitaman
format Conference or Workshop Item
author Sharma, Rajesh
Datta, Anwitaman
spellingShingle Sharma, Rajesh
Datta, Anwitaman
A tunable graph model for incorporating geographic spread in social graph models
author_sort Sharma, Rajesh
title A tunable graph model for incorporating geographic spread in social graph models
title_short A tunable graph model for incorporating geographic spread in social graph models
title_full A tunable graph model for incorporating geographic spread in social graph models
title_fullStr A tunable graph model for incorporating geographic spread in social graph models
title_full_unstemmed A tunable graph model for incorporating geographic spread in social graph models
title_sort tunable graph model for incorporating geographic spread in social graph models
publishDate 2013
url https://hdl.handle.net/10356/98708
http://hdl.handle.net/10220/12672
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