Appliance of Social Network Analysis and Data Visualization Techniques in Analysis of Information Propagation

2019 ◽  
pp. 131-143
Author(s):  
Leo Mrsic ◽  
Srecko Zajec ◽  
Robert Kopal
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Data Visualization ◽  
Information Propagation ◽  
Visualization Techniques

scholarly journals Joint Link Prediction and Network Alignment via Cross-graph Embedding

2019 ◽  
Author(s):  
Xingbo Du ◽  
Junchi Yan ◽  
Hongyuan Zha
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Link Prediction ◽  
Graph Embedding ◽  
Network Alignment ◽  
Initial Vertex ◽  
Information Propagation ◽  
Graph Node ◽  
Embedding Technique

Link prediction and network alignment are two important problems in social network analysis and other network related applications. Considerable efforts have been devoted to these two problems while often in an independent way to each other. In this paper we argue that these two tasks are relevant and present a joint link prediction and network alignment framework, whereby a novel cross-graph node embedding technique is devised to allow for information propagation. Our approach can either work with a few initial vertex correspondence as seeds, or from scratch. By extensive experiments on public benchmark, we show that link prediction and network alignment can benefit to each other especially for improving the recall for both tasks.

A sociogram is worth a thousand words: proposing a method for the visual analysis of narrative data

2017 ◽  
Vol 18 (1) ◽  
pp. 70-87 ◽  
Author(s):  
Damien Contandriopoulos ◽  
Catherine Larouche ◽  
Mylaine Breton ◽  
Astrid Brousselle
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Data Visualization ◽  
Analytical Method ◽  
Visual Analysis ◽  
Underlying Structure ◽  
Innovative Method ◽  
Graph Optimization ◽  
Quantitative And Qualitative Methods

This article proposes an innovative method for the visual analysis of narrative data that involves three steps: transforming narrative data into relational data, creating two-mode networks displayed with graph optimization algorithms derived from social network analysis (SNA), and visually analyzing sociograms. We argue that understanding how actors and their opinions constitute a network-like structure opens up promising avenues for interpreting data. This approach provides powerful data visualization that facilitates inductive identification of the underlying structure of narrative data. It also reveals the complexities of the links between differently positioned actors in a structure that a personal attribute-based analytical method might overlook. Lastly, it can be productively combined with other quantitative and qualitative methods to make sense of narrative data.

Ikenet: Social network analysis of e-mail traffic in the Eisenhower Leadership Development Program

2007 ◽  
Author(s):  
Ian McCulloh ◽  
Grace Garcia ◽  
Kelsey Tardieu ◽  
Jennifer MacGibbon ◽  
Heather Dye ◽  
...  
Keyword(s):  
Social Network ◽  
Leadership Development ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Development Program ◽  
Leadership Development Program ◽  
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