Longitudinal Network Analysis of a Peer to Peer Community

2011 ◽  
Vol 6 (3) ◽  
pp. 202-208 ◽  
Author(s):  
Weijia You ◽  
Lu Liu ◽  
Chenggong Lv
Keyword(s):  
Network Analysis ◽  
Peer To Peer ◽  
Longitudinal Network Analysis ◽  
Peer Community

Dynamics and Evolutional Patterns of Social Networks

2011 ◽  
pp. 156-170
Author(s):  
Yingzi Jin ◽  
Yutaka Matsuo
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Analysis ◽  
Real World ◽  
Dynamic Network ◽  
Network Evolution ◽  
Belief Formation ◽  
Network Mining ◽  
Longitudinal Network Analysis ◽  
Relational Network

Previous chapters focused on the models of static networks, which consider a relational network at a given point in time. However, real-world social networks are dynamic in nature; for example, friends of friends become friends. Social network research has, in recent years, paid increasing attention to dynamic and longitudinal network analysis in order to understand network evolution, belief formation, friendship formation, and so on. This chapter focuses mainly on the dynamics and evolutional patterns of social networks. The chapter introduces real-world applications and reviews major theories and models of dynamic network mining.

scholarly journals A theoretical and empirical comparison of the temporal exponential random graph model and the stochastic actor-oriented model

2019 ◽  
Vol 7 (1) ◽  
pp. 20-51 ◽  
Author(s):  
Philip Leifeld ◽  
Skyler J. Cranmer
Keyword(s):  
Network Analysis ◽  
Random Graph ◽  
Graph Model ◽  
Real Data ◽  
The Other ◽  
Exponential Random Graph Model ◽  
Empirical Comparison ◽  
Random Graph Model ◽  
Exponential Random Graph ◽  
Longitudinal Network Analysis

AbstractThe temporal exponential random graph model (TERGM) and the stochastic actor-oriented model (SAOM, e.g., SIENA) are popular models for longitudinal network analysis. We compare these models theoretically, via simulation, and through a real-data example in order to assess their relative strengths and weaknesses. Though we do not aim to make a general claim about either being superior to the other across all specifications, we highlight several theoretical differences the analyst might consider and find that with some specifications, the two models behave very similarly, while each model out-predicts the other one the more the specific assumptions of the respective model are met.

scholarly journals Creating a Social Learning Environment for and by Older Adults in the Use and Adoption of Smartphone Technology to Age in Place

2021 ◽  
Vol 9 ◽  
Author(s):  
Marjolein den Haan ◽  
Rens Brankaert ◽  
Gail Kenning ◽  
Yuan Lu
Keyword(s):  
Older Adults ◽  
Focus Group ◽  
Learning Community ◽  
Sustainable Use ◽  
Smartphone Application ◽  
Peer To Peer ◽  
Community Learning ◽  
Age In Place ◽  
Smartphone Technology ◽  
Peer Community

Smartphone technologies can support older adults in their daily lives as they age in place at home. However, they may struggle to use these technologies which impacts acceptance, adoption, and sustainable use. Peer to peer community learning has the potential to support older adults to learn using (smartphone) technologies. This paper studies such a learning community approach and how it can support older adults to learn using and adopt the smartphone application GoLivePhone. This technology assists older adults in their daily living by supporting them through fall detection and activity tracking. In particular, the interface of this application can evolve and adapt as older adults become more knowledgeable during the use process or as their abilities change. This paper shows a field study with seven older adults learning and using the GoLivePhone technology through a living lab approach. These older adults participated in this research in a technology learning community that was set-up for research purposes. For this we used ordinary Samsung A3 smartphones with the simplified GoLivePhone software, particularly designed for older adults. At the end of the learning class we conducted an additional focus group to both explore factors facilitating older adults to learn using this technology and to identify their main personal drivers and motivators to start and adopt this technology. We collected qualitative data via open questions and audio recording during the focus group. This collected data was subject to a thematic analysis, coding was primarily performed by the first author, and reviewed by the other authors. We provide insights into how peer to peer community learning can contribute, and found both super-users and recall tools to be helpful to support sustainable use of smartphone technology to support older adults to age in place.

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