NDLI: Who were you talking to - Mining interpersonal relationships from cellphone network data

Content Provider	IEEE Xplore Digital Library
Author	Mo Yu Wenjun Si Guojie Song Zhenhui Li Yen, J.
Copyright Year	2014
Description	Author affiliation: Coll. of Inf. Sci. & Technol., Pennsylvania State Univ., University Park, PA, USA (Mo Yu; Zhenhui Li; Yen, J.) \|\| Key Lab. of Machine Perception, Peking Univ., Beijing, China (Wenjun Si; Guojie Song)
Abstract	People play different roles in various social networks. Even in a single network, people may interact with others based on different roles, and there are various relationships among them. However, current research usually treats all relationships homogeneously (i.e. friendship). In this paper, we try to identify different types of relationship (family, colleague, and social) within social networks. By analyzing a large-scale cellphone network, we gain insights about human mobility patterns. We design three metrics to capture colocation behaviors for cellphone users, taking spatial-temporal factors into consideration. These metrics show that users with different relationships demonstrate significantly different co-locating patterns. With these metrics as features, we adopt supervised approach to classify cellphone user pairs into different relationship categories. Comparing to using network and communication features, co-location metrics demonstrate better performance to fulfill the task of relationship identification.
Starting Page	485
Ending Page	490
File Size	151595
Page Count	6
File Format	PDF
ISBN	9781479958771
DOI	10.1109/ASONAM.2014.6921630
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Measurement Support vector machines Radio frequency Spatial-Temporal Analyses Accuracy Cellphone Network Social network services Conferences Educational institutions Relationship Identification
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Identifying user attributes through non-i.i.d. multi-instance learning

Local Community Identification in Social Networks

Battling the Internet water army: Detection of hidden paid posters

Understanding Social Machines

Inferring Social Relationships across Social Networks for Viral Marketing

ETAF: An extended trust antecedents framework for trust prediction

Building a Semantic Social Network Based on Interpersonal Relationships

Activity profiles in online social media

Exploring UK crime networks

Who were you talking to - Mining interpersonal relationships from cellphone network data

Similar Documents

Identifying user attributes through non-i.i.d. multi-instance learning

Local Community Identification in Social Networks

Battling the Internet water army: Detection of hidden paid posters

Understanding Social Machines

Inferring Social Relationships across Social Networks for Viral Marketing

ETAF: An extended trust antecedents framework for trust prediction

Building a Semantic Social Network Based on Interpersonal Relationships

Activity profiles in online social media

Exploring UK crime networks

Who were you talking to - Mining interpersonal relationships from cellphone network data