Topic Modeling in Large Scale Social Network Data

2015 ◽  
Author(s):  
Aman Ahuja ◽  
Wei Wei ◽  
Kathleen M. Carley
Keyword(s):  
Social Network ◽  
Topic Modeling ◽  
Large Scale ◽  
Network Data ◽  
Social Network Data

Learning Distilled Graph for Large-Scale Social Network Data Clustering

2020 ◽  
Vol 32 (7) ◽  
pp. 1393-1404
Author(s):  
Wenhe Liu ◽  
Dong Gong ◽  
Mingkui Tan ◽  
Javen Qinfeng Shi ◽  
Yi Yang ◽  
...  
Keyword(s):  
Social Network ◽  
Data Clustering ◽  
Large Scale ◽  
Network Data ◽  
Social Network Data

scholarly journals Distributed Centrality Analysis of Social Network Data Using MapReduce

Algorithms ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 161 ◽  
Author(s):  
Ranjan Kumar Behera ◽  
Santanu Kumar Rath ◽  
Sanjay Misra ◽  
Robertas Damaševičius ◽  
Rytis Maskeliūnas
Keyword(s):  
Social Network ◽  
Large Scale ◽  
Network Data ◽  
Network Centrality ◽  
Efficient Computation ◽  
Social Network Data ◽  
Distributed Approach ◽  
Hadoop Platform ◽  
Behavior Network ◽  
Centrality Analysis

Analyzing the structure of a social network helps in gaining insights into interactions and relationships among users while revealing the patterns of their online behavior. Network centrality is a metric of importance of a network node in a network, which allows revealing the structural patterns and morphology of networks. We propose a distributed computing approach for the calculation of network centrality value for each user using the MapReduce approach in the Hadoop platform, which allows faster and more efficient computation as compared to the conventional implementation. A distributed approach is scalable and helps in efficient computations of large-scale datasets, such as social network data. The proposed approach improves the calculation performance of degree centrality by 39.8%, closeness centrality by 40.7% and eigenvalue centrality by 41.1% using a Twitter dataset.

The Social Pipeline: How Friend Influence and Peer Exposure Widen the STEM Gender Gap

2019 ◽  
Vol 92 (2) ◽  
pp. 105-123 ◽  
Author(s):  
Isabel J. Raabe ◽  
Zsófia Boda ◽  
Christoph Stadtfeld
Keyword(s):  
Social Network ◽  
Social Context ◽  
Panel Data ◽  
Gender Gap ◽  
Large Scale ◽  
Network Data ◽  
Gender Inequalities ◽  
Social Network Data ◽  
Science Technology ◽  
The Social

Individuals’ favorite subjects in school can predetermine their educational and occupational careers. If girls develop weaker preferences for science, technology, engineering, and math (STEM), it can contribute to macrolevel gender inequalities in income and status. Relying on large-scale panel data on adolescents from Sweden (218 classrooms, 4,998 students), we observe a widening gender gap in preferring STEM subjects within a year (girls, 19 to 15 percent; boys, 21 to 20 percent). By applying newly developed random-coefficient multilevel stochastic actor-oriented models on social network data (27,428 friendships), we investigate how social context contributes to those changes. We find strong evidence that students adjust their preferences to those of their friends (friend influence). Moreover, girls tend to retain their STEM preferences when other girls in their classroom also like STEM (peer exposure). We conclude that these mechanisms amplify preexisting preferences and thereby contribute to the observed dramatic widening of the STEM gender gap.

Detecting Local Communities within a Large Scale Social Network Using Mapreduce

2014 ◽  
Vol 10 (1) ◽  
pp. 57-76 ◽  
Author(s):  
Hongjun Yin ◽  
Jing Li ◽  
Yue Niu
Keyword(s):  
Social Network ◽  
Large Scale ◽  
Local Algorithm ◽  
Network Data ◽  
Data Sets ◽  
Network Partitioning ◽  
Personalized Pagerank ◽  
Social Network Data ◽  
Programming Framework ◽  
The Social

Social network partitioning has become a very important function. One objective for partitioning is to identify interested communities to target for marketing and advertising activities. The bottleneck to detection of these communities is the large scalability of the social network. Previous methods did not effectively address the problem because they considered the overall network. Social networks have strong locality, so designing a local algorithm to find an interested community to address this objective is necessary. In this paper, we develop a local partition algorithm, named, Personalized PageRank Partitioning, to identify the community. We compute the conductance of the social network with a Personalized PageRank and Markov chain stationary distribution of the social network, and then sweep the conductance to find the smallest cut. The efficiency of the cut can reach. In order to detect a larger scale social network, we design and implement the algorithm on a MapReduce-programming framework. Finally, we execute our experiment on several actual social network data sets and compare our method to others. The experimental results show that our algorithm is feasible and very effective.

Abstract #1249: Novel Use of Physician Social Network Data to Map the Gap in the Endocrinology Workforce

2015 ◽  
Vol 21 ◽  
pp. 301
Author(s):  
Armand Krikorian ◽  
Lily Peng ◽  
Zubair Ilyas ◽  
Joumana Chaiban
Keyword(s):  
Social Network ◽  
Network Data ◽  
Social Network Data

The Multilevel p2 Model

Methodology ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 42-47 ◽  
Author(s):  
Bonne J. H. Zijlstra ◽  
Marijtje A. J. van Duijn ◽  
Tom A. B. Snijders
Keyword(s):  
Social Network ◽  
Network Data ◽  
Mcmc Algorithm ◽  
Independent Observation ◽  
Single Observation ◽  
Multiple Networks ◽  
Social Network Data ◽  
Multiple Observations ◽  
Practical Support ◽  
The Social

The p 2 model is a random effects model with covariates for the analysis of binary directed social network data coming from a single observation of a social network. Here, a multilevel variant of the p 2 model is proposed for the case of multiple observations of social networks, for example, in a sample of schools. The multilevel p 2 model defines an identical p 2 model for each independent observation of the social network, where parameters are allowed to vary across the multiple networks. The multilevel p 2 model is estimated with a Bayesian Markov Chain Monte Carlo (MCMC) algorithm that was implemented in free software for the statistical analysis of complete social network data, called StOCNET. The new model is illustrated with a study on the received practical support by Dutch high school pupils of different ethnic backgrounds.

scholarly journals Social network data of Swiss farmers related to agricultural climate change mitigation

Data in Brief ◽  
2021 ◽  
Vol 35 ◽  
pp. 106898
Author(s):  
Cordelia Sophie Kreft ◽  
Mario Angst ◽  
Robert Huber ◽  
Robert Finger
Keyword(s):  
Climate Change ◽  
Social Network ◽  
Climate Change Mitigation ◽  
Network Data ◽  
Social Network Data ◽  
Change Mitigation

scholarly journals Social Networks and Loneliness During the COVID-19 Pandemic

2021 ◽  
Vol 7 ◽  
pp. 237802312098525
Author(s):  
Balazs Kovacs ◽  
Nicholas Caplan ◽  
Samuel Grob ◽  
Marissa King
Keyword(s):  
Social Network ◽  
Social Isolation ◽  
Close Relationships ◽  
Network Size ◽  
Global Network ◽  
Network Data ◽  
Network Characteristics ◽  
Face To Face ◽  
Social Network Data ◽  
Social Network Size

We utilize longitudinal social network data collected pre–COVID-19 in June 2019 and compare them with data collected in the midst of COVID in June 2020. We find significant decreases in network density and global network size following a period of profound social isolation. While there is an overall increase in loneliness during this era, certain social network characteristics of individuals are associated with smaller increases in loneliness. Specifically, we find that people with fewer than five “very close” relationships report increases in loneliness. We further find that face-to-face interactions, as well as the duration and frequency of interactions with very close ties, are associated with smaller increases in loneliness during the pandemic. We also report on factors that do not moderate the effect of social isolation on perceived loneliness, such as gender, age, or overall social network size.

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