A Longitudinal Social Network Clustering Method Based on Tie Strength

Author(s):  
Zhiyong Zhang ◽  
Mao Ye ◽  
Yijie Huang ◽  
Nan Sun
Author(s):  
Alexander Troussov ◽  
Sergey Maruev ◽  
Sergey Vinogradov ◽  
Mikhail Zhizhin

Techno-social systems generate data, which are rather different, than data, traditionally studied in social network analysis and other fields. In massive social networks agents simultaneously participate in several contexts, in different communities. Network models of many real data from techno-social systems reflect various dimensionalities and rationales of actor's actions and interactions. The data are inherently multidimensional, where “everything is deeply intertwingled”. The multidimensional nature of Big Data and the emergence of typical network characteristics in Big Data, makes it reasonable to address the challenges of structure detection in network models, including a) development of novel methods for local overlapping clustering with outliers, b) with near linear performance, c) preferably combined with the computation of the structural importance of nodes. In this chapter the spreading connectivity based clustering method is introduced. The viability of the approach and its advantages are demonstrated on the data from the largest European social network VK.


Societies ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 13
Author(s):  
Emmelyn A. J. Croes ◽  
Marjolijn L. Antheunis

This study examined which media people use on a day-to-day basis to communicate and whether tie strength influenced this media use. Furthermore, we analyzed whether online and offline interactions differ in perceived intimacy and whether tie strength impacts perceived interaction intimacy: 347 real interactions of 9 participants (3 male, 6 female) were analyzed; 172 online (WhatsApp, Facebook Messenger, email, SMS interactions) and 175 offline (recorded phone and face-to-face conversations). The results revealed that the participants communicated most frequently face-to-face or via WhatsApp, especially with strong ties. Furthermore, participants rated their interactions with strong ties as more intimate compared to weak-tie interactions. Our findings have implications for Social Information Processing theory, as our findings show that people are equally able to communicate intimate messages online and offline.


Author(s):  
Derk Bransen ◽  
Marjan J. B. Govaerts ◽  
Dominique M. A. Sluijsmans ◽  
Jeroen Donkers ◽  
Piet G. C. Van den Bossche ◽  
...  

Abstract Introduction Recent conceptualizations of self-regulated learning acknowledge the importance of co-regulation, i.e., students’ interactions with others in their networks to support self-regulation. Using a social network approach, the aim of this study is to explore relationships between characteristics of medical students’ co-regulatory networks, perceived learning opportunities, and self-regulated learning. Methods The authors surveyed 403 undergraduate medical students during their clinical clerkships (response rate 65.5%). Using multiple regression analysis, structural equation modelling techniques, and analysis of variance, the authors explored relationships between co-regulatory network characteristics (network size, network diversity, and interaction frequency), students’ perceptions of learning opportunities in the workplace setting, and self-reported self-regulated learning. Results Across all clerkships, data showed positive relationships between tie strength and self-regulated learning (β = 0.095, p < 0.05) and between network size and tie strength (β = 0.530, p < 0.001), and a negative relationship between network diversity and tie strength (β = −0.474, p < 0.001). Students’ perceptions of learning opportunities showed positive relationships with both self-regulated learning (β = 0.295, p < 0.001) and co-regulatory network size (β = 0.134, p < 0.01). Characteristics of clerkship contexts influenced both co-regulatory network characteristics (size and tie strength) and relationships between network characteristics, self-regulated learning, and students’ perceptions of learning opportunities. Discussion The present study reinforces the importance of co-regulatory networks for medical students’ self-regulated learning during clinical clerkships. Findings imply that supporting development of strong networks aimed at frequent co-regulatory interactions may enhance medical students’ self-regulated learning in challenging clinical learning environments. Social network approaches offer promising ways of further understanding and conceptualising self- and co-regulated learning in clinical workplaces.


Author(s):  
Poonam Rani ◽  
MPS Bhatia ◽  
Devendra K Tayal

The paper presents an intelligent approach for the comparison of social networks through a cone model by using the fuzzy k-medoids clustering method. It makes use of a geometrical three-dimensional conical model, which astutely represents the user experience views. It uses both the static as well as the dynamic parameters of social networks. In this, we propose an algorithm that investigates which social network is more fruitful. For the experimental results, the proposed work is employed on the data collected from students from different universities through the Google forms, where students are required to rate their experience of using different social networks on different scales.


2018 ◽  
Vol 5 (3) ◽  
pp. 67-86
Author(s):  
Eya Ben Ahmed

This article describes how thanks to the technological development, social media has propagated in recent years. The latter describes a range of Web-based platforms that enable people to socially interact with one another online. Several types of social media appeared. In this context, the author focuses on scientific social network which connects the researchers and allow them to communicate and collaborate online. In this paper, we, particularly, aim to detect the scientific leaders through firstly detect communities in social network then identify the leader of each group. To do this, the author introduces a new hierarchical semi-supervised clustering method based on ordinal density. The results of carried out experiments on real scientific warehouse have shown significant profits in terms of accuracy and performance.


2016 ◽  
Vol 46 (2) ◽  
pp. 250-272 ◽  
Author(s):  
Hai Liang ◽  
King-wa Fu

It remains controversial whether community structures in social networks are beneficial or not for information diffusion. This study examined the relationships among four core concepts in social network analysis—network redundancy, information redundancy, ego-alter similarity, and tie strength—and their impacts on information diffusion. By using more than 6,500 representative ego networks containing nearly 1 million following relationships from Twitter, the current study found that (1) network redundancy is positively associated with the probability of being retweeted even when competing variables are controlled for; (2) network redundancy is positively associated with information redundancy, which in turn decreases the probability of being retweeted; and (3) the inclusion of both ego-alter similarity and tie strength can attenuate the impact of network redundancy on the probability of being retweeted.


2017 ◽  
Vol 186 (7) ◽  
pp. 796-804 ◽  
Author(s):  
Holly B. Shakya ◽  
Christopher J. Fariss ◽  
Christopher Ojeda ◽  
Anita Raj ◽  
Elizabeth Reed

2015 ◽  
Vol 28 (3) ◽  
pp. 189-212
Author(s):  
Roya Asadi ◽  
Sameem Abdul Kareem ◽  
Mitra Asadi ◽  
Shokoofeh Asadi

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