scholarly journals Estimating tie strength in social networks using temporal communication data

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Javier Ureña-Carrion ◽  
Jari Saramäki ◽  
Mikko Kivelä
Keyword(s):  
Time Series ◽  
Social Network ◽  
Contact Time ◽  
Temporal Stability ◽  
Strong Relationship ◽  
Tie Strength ◽  
Topological Features ◽  
Social Network Structure ◽  
Temporal Features ◽  
Rich Data

AbstractEven though the concept of tie strength is central in social network analysis, it is difficult to quantify how strong social ties are. One typical way of estimating tie strength in data-driven studies has been to simply count the total number or duration of contacts between two people. This, however, disregards many features that can be extracted from the rich data sets used for social network reconstruction. Here, we focus on contact data with temporal information. We systematically study how features of the contact time series are related to topological features usually associated with tie strength. We focus on a large mobile-phone dataset and measure a number of properties of the contact time series for each tie and use these to predict the so-called neighbourhood overlap, a feature related to strong ties in the sociological literature. We observe a strong relationship between temporal features and the neighbourhood overlap, with many features outperforming simple contact counts. Features that stand out include the number of days with calls, number of bursty cascades, typical times of contacts, and temporal stability. These are also seen to correlate with the overlap in diverse smaller communication datasets studied for reference. Taken together, our results suggest that such temporal features could be useful for inferring social network structure from communication data.

scholarly journals Understanding and Analyzing Social Network Structure Among University Students

2021 ◽  
Author(s):  
Md. Sharif Hossen ◽  
Md. Aminul Islam
Keyword(s):  
University Students ◽  
Mobile Phone ◽  
Strong Relationship ◽  
Mobile Phone Data ◽  
Human Beings ◽  
Human Communication ◽  
Multiple Sensors ◽  
Social Network Structure ◽  
Rich Data ◽  
Public Health Epidemiology

Abstract Mobile phone arguably is one of the most reached and used technology in human history. Technology has become ubiquitous in the life of human beings. Equipped with multiple sensors and devices, smartphones can record each and every action, psychological and environmental states of users, making it a goldmine of rich data about and insight into the dynamics of human communication, human behavior, relationships, and social interaction. As a source of data for empirical research, this device has gotten much attention from scholars in various disciplines like sociology, social psychology, urban studies, communication and media studies, public health, epidemiology, and computer science. This research tries to understand the structure of social networks of university students by investigating their communication patterns using self-reported mobile phone data. We collected behavioral data for one month using a Call Log Analytics mobile phone app. The data contained information about respondents’ contacts, date and time of call, duration of the call, call type (e.g., incoming, outgoing, missed), and frequency of the call. We used UCINET to analyze the data. In this investigation, we can find those students who are connected to most of the classmates and maintain a strong relationship and perform a task successfully using the values of eigenvector, closeness, and betweenness centrality, respectively. Moreover, this study also helps us to find out the pattern of the students using contact duration, incoming and outgoing calls.

Evolutionary dynamics modeling of symbolic social network structure equilibrium

2020 ◽  
Vol 17 (10) ◽  
pp. 229-240
Author(s):  
Weijin Jiang ◽  
Sijian Lv ◽  
Yirong Jiang ◽  
Jiahui Chen ◽  
Fang Ye ◽  
...  
Keyword(s):  
Social Network ◽  
Network Structure ◽  
Evolutionary Dynamics ◽  
Dynamics Modeling ◽  
Social Network Structure

Online Communities and Social Network Structure

2019 ◽  
Author(s):  
Yanhao Wei ◽  
Wensi Zhang ◽  
Sha Yang ◽  
Xi Chen
Keyword(s):  
Social Network ◽  
Network Structure ◽  
Online Communities ◽  
Social Network Structure

scholarly journals Perceived Intimacy Differences of Daily Online and Offline Interactions in People’s Social Network

Societies ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 13
Author(s):  
Emmelyn A. J. Croes ◽  
Marjolijn L. Antheunis
Keyword(s):  
Information Processing ◽  
Social Network ◽  
Social Information Processing ◽  
Media Use ◽  
Tie Strength ◽  
Information Processing Theory ◽  
Strong Ties ◽  
Social Information Processing Theory ◽  
Face To Face ◽  
Processing Theory

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.

scholarly journals Relationships between medical students’ co-regulatory network characteristics and self-regulated learning: a social network study

2021 ◽  
Author(s):  
Derk Bransen ◽  
Marjan J. B. Govaerts ◽  
Dominique M. A. Sluijsmans ◽  
Jeroen Donkers ◽  
Piet G. C. Van den Bossche ◽  
...  
Keyword(s):  
Social Network ◽  
Medical Students ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Learning Opportunities ◽  
Network Size ◽  
Tie Strength ◽  
Network Characteristics ◽  
Self Regulated Learning ◽  
Regulated Learning

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.

scholarly journals Topological Data Analysis Approaches to Uncovering the Timing of Ring Structure Onset in Filamentous Networks

2021 ◽  
Vol 83 (3) ◽  
Author(s):  
Maria-Veronica Ciocanel ◽  
Riley Juenemann ◽  
Adriana T. Dawes ◽  
Scott A. McKinley
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Actin Filaments ◽  
Time Series Data ◽  
Polymer Networks ◽  
Topological Data Analysis ◽  
Series Data ◽  
Topological Features ◽  
Topological Data

AbstractIn developmental biology as well as in other biological systems, emerging structure and organization can be captured using time-series data of protein locations. In analyzing this time-dependent data, it is a common challenge not only to determine whether topological features emerge, but also to identify the timing of their formation. For instance, in most cells, actin filaments interact with myosin motor proteins and organize into polymer networks and higher-order structures. Ring channels are examples of such structures that maintain constant diameters over time and play key roles in processes such as cell division, development, and wound healing. Given the limitations in studying interactions of actin with myosin in vivo, we generate time-series data of protein polymer interactions in cells using complex agent-based models. Since the data has a filamentous structure, we propose sampling along the actin filaments and analyzing the topological structure of the resulting point cloud at each time. Building on existing tools from persistent homology, we develop a topological data analysis (TDA) method that assesses effective ring generation in this dynamic data. This method connects topological features through time in a path that corresponds to emergence of organization in the data. In this work, we also propose methods for assessing whether the topological features of interest are significant and thus whether they contribute to the formation of an emerging hole (ring channel) in the simulated protein interactions. In particular, we use the MEDYAN simulation platform to show that this technique can distinguish between the actin cytoskeleton organization resulting from distinct motor protein binding parameters.

scholarly journals A Novel Time-Sensitive Composite Similarity Model for Multivariate Time-Series Correlation Analysis

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 731
Author(s):  
Mengxia Liang ◽  
Xiaolong Wang ◽  
Shaocong Wu
Keyword(s):  
Time Series ◽  
Correlation Analysis ◽  
Stock Price ◽  
Multivariate Time Series ◽  
Temporal Features ◽  
Proposed Model ◽  
Time Series Segmentation ◽  
Similarity Model ◽  
Dynamic Time ◽  
Investment Portfolios

Finding the correlation between stocks is an effective method for screening and adjusting investment portfolios for investors. One single temporal feature or static nontemporal features are generally used in most studies to measure the similarity between stocks. However, these features are not sufficient to explore phenomena such as price fluctuations similar in shape but unequal in length which may be caused by multiple temporal features. To research stock price volatilities entirely, mining the correlation between stocks should be considered from the point view of multiple features described as time series, including closing price, etc. In this paper, a time-sensitive composite similarity model designed for multivariate time-series correlation analysis based on dynamic time warping is proposed. First, a stock is chosen as the benchmark, and the multivariate time series are segmented by the peaks and troughs time-series segmentation (PTS) algorithm. Second, similar stocks are screened out by similarity. Finally, the rate of rising or falling together between stock pairs is used to verify the proposed model’s effectiveness. Compared with other models, the composite similarity model brings in multiple temporal features and is generalizable for numerical multivariate time series in different fields. The results show that the proposed model is very promising.

scholarly journals Time+User Dual Attention Based Sentiment Prediction for Multiple Social Network Texts With Time Series

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 17644-17653 ◽  
Author(s):  
Lei Li ◽  
Yabin Wu ◽  
Yuwei Zhang ◽  
Tianyuan Zhao
Keyword(s):  
Time Series ◽  
Social Network

scholarly journals Inequality is rising where social network segregation interacts with urban topology

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Gergő Tóth ◽  
Johannes Wachs ◽  
Riccardo Di Clemente ◽  
Ákos Jakobi ◽  
Bence Ságvári ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Structure ◽  
Urban Geography ◽  
Online Social Network ◽  
Stage Model ◽  
Significant Relationships ◽  
Social Segregation ◽  
Social Network Structure ◽  
Physical Barriers

AbstractSocial networks amplify inequalities by fundamental mechanisms of social tie formation such as homophily and triadic closure. These forces sharpen social segregation, which is reflected in fragmented social network structure. Geographical impediments such as distance and physical or administrative boundaries also reinforce social segregation. Yet, less is known about the joint relationships between social network structure, urban geography, and inequality. In this paper we analyze an online social network and find that the fragmentation of social networks is significantly higher in towns in which residential neighborhoods are divided by physical barriers such as rivers and railroads. Towns in which neighborhoods are relatively distant from the center of town and amenities are spatially concentrated are also more socially segregated. Using a two-stage model, we show that these urban geography features have significant relationships with income inequality via social network fragmentation. In other words, the geographic features of a place can compound economic inequalities via social networks.

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