publication . Conference object . Preprint . 2016

Role of Temporal Diversity in Inferring Social Ties Based on Spatio-Temporal Data

Harsh Nisar; Deshana Desai; Rishabh Bhardawaj;
Open Access
  • Published: 10 Nov 2016
  • Publisher: ACM Press
Abstract
The last two decades have seen a tremendous surge in research on social networks and their implications. The studies includes inferring social relationships, which in turn have been used for target advertising, recommendations, search customization etc. However, the offline experiences of human, the conversations with people and face-to-face interactions that govern our lives interactions have received lesser attention. We introduce DAIICT Spatio-Temporal Network (DSSN), a spatiotemporal dataset of 0.7 million data points of continuous location data logged at an interval of every 2 minutes by mobile phones of 46 subjects. Our research is focused at inferring rel...
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doi: 10.1145/3041823.3041836
Subjects
free text keywords: Computer Science - Social and Information Networks, Interpersonal ties, Data point, Social network, business.industry, business, Bounded function, Data science, Temporal database, Machine learning, computer.software_genre, computer, Personalization, Population, education.field_of_study, education, Artificial intelligence, Socialization, Computer science
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http://arxiv.org/pdf/1611.0329...
Conference object
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arXiv.org e-Print Archive
Preprint . 2016
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https://dblp.uni-trier.de/db/j...
Conference object
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Conference object . 2017
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[1] D. Crandall, L. Backstrom, D. Cosley, S. Suri, D. Huttenlocher and J. Kleinberg- Inferring social ties from geographic coincidences. Proc. National Academy of Sciences,December, 2010. [OpenAIRE]

[2] Nathan Eagle , Alex (Sandy) Pentland , David LazerInferring Social Network Structure using Mobile Phone Data.

[3] Charles Blundell, Katherine A. Heller, Je rey M. BeckModelling Reciprocating Relationships with Hawkes Processes.

[4] Zhenyu Wu , Ming Zou- An incremental community detection method for social tagging systems using locality-sensitive hashing.

[5] Ivan Brugere, Venkata M. V. Gunturi, Shashi ShekharModeling and analysis of spatio-temporal social networks.

[6] Huy Pham, Cyrus Shahabi, Yan Liu- EBM - An Entropy-Based Model to Infer Social Strength from Spatiotemporal Data.

[7] Huy Pham, Ling Hu, Cyrus Shahabi- Towards Integrating Real-World Spatiotemporal Data with Social Networks.

[8] Nancy Katz, David Lazer, Holly Arrow, Noshir Contractor- Network Theory and Small Groups.

[9] Quannan Li, Yu Zheng, Xing Xie,Yukun Chen, Wenyu Liu, Wei-Ying Ma- Mining User Similarity Based on Location History.

[10] Tobias Hecking, Tilman GA~ u}hnert, Sam Zeini, Ulrich Hoppe- Task and Time Aware Community Detection in Dynamically Evolving Social Networks.

[11] Wikipedia - Geohash | Wikipedia, The Free Encyclopedia, [Online; accessed 20-August-2016].

Related research
Abstract
The last two decades have seen a tremendous surge in research on social networks and their implications. The studies includes inferring social relationships, which in turn have been used for target advertising, recommendations, search customization etc. However, the offline experiences of human, the conversations with people and face-to-face interactions that govern our lives interactions have received lesser attention. We introduce DAIICT Spatio-Temporal Network (DSSN), a spatiotemporal dataset of 0.7 million data points of continuous location data logged at an interval of every 2 minutes by mobile phones of 46 subjects. Our research is focused at inferring rel...
Read more
doi: 10.1145/3041823.3041836
Subjects
free text keywords: Computer Science - Social and Information Networks, Interpersonal ties, Data point, Social network, business.industry, business, Bounded function, Data science, Temporal database, Machine learning, computer.software_genre, computer, Personalization, Population, education.field_of_study, education, Artificial intelligence, Socialization, Computer science
Related Organizations
Download fromView all 2 versions
http://arxiv.org/pdf/1611.0329...
Conference object
Provider: UnpayWall
arXiv.org e-Print Archive
Preprint . 2016
Provider: arXiv.org e-Print Archive
https://dblp.uni-trier.de/db/j...
Conference object
Provider: Microsoft Academic Graph
http://dl.acm.org/ft_gateway.c...
Conference object . 2017
Provider: Crossref

[1] D. Crandall, L. Backstrom, D. Cosley, S. Suri, D. Huttenlocher and J. Kleinberg- Inferring social ties from geographic coincidences. Proc. National Academy of Sciences,December, 2010. [OpenAIRE]

[2] Nathan Eagle , Alex (Sandy) Pentland , David LazerInferring Social Network Structure using Mobile Phone Data.

[3] Charles Blundell, Katherine A. Heller, Je rey M. BeckModelling Reciprocating Relationships with Hawkes Processes.

[4] Zhenyu Wu , Ming Zou- An incremental community detection method for social tagging systems using locality-sensitive hashing.

[5] Ivan Brugere, Venkata M. V. Gunturi, Shashi ShekharModeling and analysis of spatio-temporal social networks.

[6] Huy Pham, Cyrus Shahabi, Yan Liu- EBM - An Entropy-Based Model to Infer Social Strength from Spatiotemporal Data.

[7] Huy Pham, Ling Hu, Cyrus Shahabi- Towards Integrating Real-World Spatiotemporal Data with Social Networks.

[8] Nancy Katz, David Lazer, Holly Arrow, Noshir Contractor- Network Theory and Small Groups.

[9] Quannan Li, Yu Zheng, Xing Xie,Yukun Chen, Wenyu Liu, Wei-Ying Ma- Mining User Similarity Based on Location History.

[10] Tobias Hecking, Tilman GA~ u}hnert, Sam Zeini, Ulrich Hoppe- Task and Time Aware Community Detection in Dynamically Evolving Social Networks.

[11] Wikipedia - Geohash | Wikipedia, The Free Encyclopedia, [Online; accessed 20-August-2016].

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