Social Network De-Anonymization and Privacy Inference with Knowledge Graph Model

2019 ◽  
Vol 16 (4) ◽  
pp. 679-692 ◽  
Author(s):  
Jianwei Qian ◽  
Xiang-Yang Li ◽  
Chunhong Zhang ◽  
Linlin Chen ◽  
Taeho Jung ◽  
...  
Keyword(s):  
Social Network ◽  
Graph Model ◽  
Knowledge Graph

Optimizing Social-Topic Engagement on Social Network and Knowledge Graph

2019 ◽  
Author(s):  
Ya-Wen Teng ◽  
Yishuo Shi ◽  
Jui-Yi Tsai ◽  
Hong-Han Shuai ◽  
Chih-Hua Tai ◽  
...  
Keyword(s):  
Social Network ◽  
Knowledge Graph

scholarly journals Does Whom Patients Sit Next to During Hemodialysis Affect Whether They Request a Living Donation?

Kidney360 ◽  
2021 ◽  
pp. 10.34067/KID.0006682020
Author(s):  
Avrum Gillespie ◽  
Edward L. Fink ◽  
Heather M. Gardiner ◽  
Crystal A. Gadegbeku ◽  
Peter P. Reese ◽  
...  
Keyword(s):  
Social Network ◽  
Living Donor ◽  
Graph Model ◽  
Social Contagion ◽  
Living Donation ◽  
Exponential Random Graph Model ◽  
Relationship Formation ◽  
Random Graph Model ◽  
Seating Arrangement ◽  
Exponential Random Graph

Background: The seating arrangement of in-center hemodialysis is conducive to patients forming a relationship and a social network. We examined how in-center hemodialysis clinic seating affected patients forming relationships, whether patients formed relationships with others who have similar transplant behaviors (homophily), and whether these relationships influenced patients (social contagion) to request a living donation from family and friends outside of the clinic. Methods: In this 30-month prospective cohort study, we observed the relationships of 46 hemodialysis patients in a hemodialysis clinic. Repeated participant surveys assessed in-center transplant discussions and living donor requests. A separable temporal exponential random graph model estimated how seating, demographics, in-center transplant discussions, and living donor requests affected relationship formation via sociality and homophily. We examined whether donation requests spread via social contagion using a susceptibility-infected model. Results: For every seat apart, the odds of participants forming a relationship decreased (OR 0.74, 95% confidence interval CI [0.61, 0.90], p = 0.002). Those who requested a living donation tended to form relationships more than those who did not (sociality, OR 1.6, CI 95% [1.02, 2.6]; p = 0.04). Participants who discussed transplantation in-center were more likely to form a relationship with another participant who discussed transplantation than with someone who did not discuss transplantation (homophily, OR 1.9, CI 95% [1.03, 3.5]; p = 0.04). Five of the 36 susceptible participants made a request after forming a relationship with another patient. Conclusions: Participants formed relationships with those whom they sat next to and had similar transplant behaviors. The observed increase in in-center transplant discussions and living donation requests by the hemodialysis clinic social network members was not because of social contagion. Instead, participants who requested a living donation were more social, formed more relationships within the clinic, and discussed transplantation with each other as a function of health-behavior homophily.

scholarly journals SOCIAL METRICS APPLIED TO SMART TOURISM

2016 ◽  
Vol IV-4/W1 ◽  
pp. 117-124 ◽  
Author(s):  
O. Cervantes ◽  
E. Gutiérrez ◽  
F. Gutiérrez ◽  
J. A. Sánchez
Keyword(s):  
Social Network ◽  
Network Analysis ◽  
Semantic Network ◽  
Graph Model ◽  
Points Of Interest ◽  
Centrality Metrics ◽  
Social Metrics ◽  
The City ◽  
Smart Tourism

We present a strategy to make productive use of semantically-related social data, from a user-centered semantic network, in order to help users (tourists and citizens in general) to discover cultural heritage, points of interest and available services in a smart city. This data can be used to personalize recommendations in a smart tourism application. Our approach is based on flow centrality metrics typically used in social network analysis: flow betweenness, flow closeness and eccentricity. These metrics are useful to discover relevant nodes within the network yielding nodes that can be interpreted as suggestions (venues or services) to users. We describe the semantic network built on graph model, as well as social metrics algorithms used to produce recommendations. We also present challenges and results from a prototypical implementation applied to the case study of the City of Puebla, Mexico.

scholarly journals Personalized Mobile Information Retrieval System

10.5772/50910 ◽  
2012 ◽  
Vol 9 (1) ◽  
pp. 11 ◽  
Author(s):  
Okkyung Choi ◽  
Kangseok Kim ◽  
Duksang Wang ◽  
Hongjin Yeh ◽  
Manpyo Hong
Keyword(s):  
Information Retrieval ◽  
Social Network ◽  
Social Relations ◽  
Retrieval System ◽  
Personal Information ◽  
Graph Model ◽  
Information Retrieval System ◽  
Global Network ◽  
Mobile Environment ◽  
Mobile Information

Building a global Network Relations with the internet has made huge changes in personal information system and even comments left on a webpage of SNS(Social Network Services) are appreciated as important elements that would provide valuable information for someone. Social Network is a relation between individuals or groups, represented in a graph model, which converts the concept of psychological and social relations into a logical structure by using node and link. But, most of the current personalized systems on the basis of Social Network are built and constructed mainly in the PC environment, and the systems are neither designed nor implemented in mobile environment. Hence, the objective of this study is to propose methods of providing Personalized Mobile Information Retrieval System using NFC (Near Field Communication) Smartphone, which will be then used for Smartphone users. Besides, this study aims to verify its efficiency through a comparative analysis of existing studies.

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